In today's interconnected infrastructures, Cyber-Physical Systems (CPSs) play a critical role in domains including water distribution, agricultural production, and energy management. Modern infrastructures rely on a network of cyber-physical components—mechanical actuators, electrical sensors, and internet-connected devices—to supervise and manage operational processes. However, the increasing complexity and connectivity of these systems amplify their vulnerability to cyberattacks, necessitating robust cybersecurity measures and effective Outlier Detection (OD) methods. These methods are essential to prevent infrastructure failures, reduce environmental waste, and mitigate damages caused by malicious activities. Existing approaches often lack the integration of multiple operational metrics and context-driven techniques, hampering their effectiveness in real-world scenarios. In large CPSs—comprising hundreds or thousands of sensors, actuators, PLCs, IoT devices, and complex Control and Protection Switching Gear (CPSG)—the challenge of ensuring data quality, security, and reliability is costly. Cyberattacks frequently appear as outliers or anomalies in the data and are launched with "minimum perturbation," making their detection significantly challenging. This dissertation proposes a novel framework, multiple pipelines, and AI-based methods to develop context-driven, data-driven, and assurance-focused OD solutions. Emphasis is placed on water and agricultural systems, illustrating the proposed framework's effectiveness, particularly through enhanced decision-making, operational efficiency, and cybersecurity measures. A comprehensive survey of OD methods that employ Artificial Intelligence (AI) techniques establishes the foundational understanding of OD. This survey underscores that successful OD depends on domain knowledge, contextual factors, and assurance principles. Synthesizing these insights, the dissertation leverages synthetically generated SCADA data and GAN-produced poisoned data, as well as real-world SCADA data from Wastewater Treatment Plants (WWTPs), to identify outliers and address critical problems—such as forecasting tunnel wastewater overflows under extreme weather conditions—by applying Recurrent Neural Network (RNN)-based Deep Learning (DL) methods. Additionally, an AI-based decision support tool is introduced to detect anomalies in complex plant data and optimize operational set-points, thereby aiding Operation and Maintenance (OandM) in Water Distribution Systems (WDSs). Similarly, in Agricultural Production Systems (APSs), which traditionally rely on reactive policies and short-term solutions, integrating advanced AI-driven OD methods provides farmers with timely, data-informed decisions that account for contextual changes resulting from outlier events. Machine Learning (ML) and DL methods measure associations, correlations, and causations among global and domestic factors, aiding in the accurate prediction of agricultural production. This contextual awareness helps manage policy, optimize resource utilization, and support precision agriculture strategies. The main contributions of this dissertation include introducing a novel framework that integrates OD techniques with AI assurance and context-driven methodologies in CPSs; developing multiple pipelines and DL models that enhance anomaly detection, forecasting accuracy, and proactive decision support in WDSs and APSs; and demonstrating measurable improvements in cybersecurity, operational efficiency, and predictive capability using real-world and synthetic data. These efforts collectively foster more trustworthy and sustainable CPSs. Experimental results are recorded, evaluated, and discussed, revealing that these contributions bridge the gap between complex theoretical constructs and tangible real-world applications. / Doctor of Philosophy / Recent unprecedented AI and sensor technology advancements are transforming all domains, including Water Distribution Systems (WDSs) and Agricultural Production Systems (APSs). With Industry 4.0, WDSs and APSs are undergoing a significant digital transformation to enable data-driven monitoring and control of utility operations. Incorporating cyber elements—such as sensors, actuators, data transmitters, receivers, Programmable Logic Controllers (PLCs), and Internet of Things (IoT) devices—aims to make these Cyber-Physical Systems (CPSs) more effective in Operation and Maintenance (OandM). However, this progress comes with a trade-off, as CPSs become increasingly vulnerable to security and safety threats. For example, in 2013, hackers seized control of a small Florida dam, releasing unprocessed water into nearby communities. Furthermore, on February 5th, 2021, a Florida water treatment plant (in Oldsmar, FL) was compromised when the hacker altered the levels of sodium hydroxide (NaOH) in the water—a chemical that would severely damage human tissue. Recent targeted attacks on infrastructure in Ukraine also highlight the risks facing critical infrastructures worldwide, including WDSs. These events suggest that current control operations are largely exposed, necessitating sophisticated learning algorithms that can estimate system states, detect anomalies, and mitigate the harm caused by such intrusions. Technology has fundamentally transformed agriculture as well, significantly impacting this domain. Agriculture, a vital occupation in numerous countries, now faces increasing global population pressures. The United Nations (UN) projects the population to reach 9.7 billion by 2050, intensifying the strain on limited arable land. With only a 4% increase in cultivable land expected by 2050, farmers must do more with less. Traditional methods are insufficient to meet the soaring demands, as a 60% increase in food production is needed to feed an additional two billion people. This necessity for enhanced productivity and reduced waste drives the integration of AI into the agricultural sector. AI adoption not only accelerates efficiency but also increases production volumes, shortening the time from farm to market. This dissertation proposes novel, data- and context-driven Deep Learning (DL)-based methods and decision-support tools to enhance cybersecurity and anomaly detection within WDSs and APSs. Focusing on these critical infrastructures demonstrates how AI-driven strategies can effectively address real-world challenges and improve resilience, operational efficiency, and overall trustworthiness. The contributions of this dissertation include a framework and pipelines that incorporate contextual insights and AI assurance principles to improve anomaly detection and cybersecurity in these domains; the development of DL models tailored for identifying complex outliers and providing actionable decision-support, thereby optimizing resource allocation and ensuring sustainable operations; and validation of these approaches through experimental evaluations using real-world and synthetic data. Collectively, these efforts highlight significant improvements in reliability, efficiency, and scalability for critical infrastructure management, bridging the gap between theoretical advances in AI-driven anomaly detection and their practical application in WDSs and APSs.