The transition towards flexible work arrangements, notably work-from-home (WFH) practices, has prompted significant discourse on their potential to reshape urban landscapes. While existing urban growth models (UGM) offer insights into environmental and economic impacts, There is a need to study the urban phenomena from the bottom-up style, considering the essential influence of individuals' behavior and decision-making process at disaggregate and local levels (Brail, 2008, p. 89). Addressing this gap, this study aims to comprehensively understand how evolving work modalities influence the urban form and land use patterns by focusing on socioeconomic and environmental factors. This research employs an Agent-Based Model (ABM) and Artificial Neural Network (ANN), integrated with GIS technologies, to predict the future Land Use and Land Cover (LULC) changes within Roanoke, Virginia. The study uniquely explores the dynamic interplay between macro-level policies and micro-level individual behaviors—categorized by employment types, social activities, and residential choices—shedding light on their collective impact on urban morphology. Contrary to conventional expectations, findings reveal that the current low rate in WFH practices has not significantly redirected urban development trends towards sprawl but rather has emphasized urban densification, largely influenced by on-site work modalities. This observation is corroborated by WFH ratios not exceeding 10% in any analyzed census tract. Regarding model performance, the integration of micro-agents into the model substantially improved its accuracy from 86% to 89.78%, enabling a systematic analysis of residential preferences between WFH and on-site working (WrOS) agents. Furthermore, logistic regression analysis and decision score maps delineate the distinct spatial preferences of these agent groups, highlighting a pronounced suburban and rural preference among WFH agents, in contrast to the urban-centric inclination of WrOS agents. Utilizing ABM and ANN integrated with GIS technologies, this research advances the precision and complexity of urban growth predictions. The findings contribute valuable insights for urban planners and policymakers and underline the intricate relationships between work modalities and urban structure, challenging existing paradigms and setting a precedent for future urban planning methodologies. / Doctor of Philosophy / As more people start working from home, cities might change unexpectedly. This study in Roanoke, Virginia, explores how work-from-home (WFH) practices affect urban development. Traditional city growth models look at big-picture trends, but this study dives into the details of workers' individual behaviors and their residential choices. Using advanced computer models such as machine learning and geographic information systems (GIS), predictions are made on how different work arrangements influence where workers live and how cities expand. Surprisingly, fewer people work from home than expected. This hasn't caused cities to spread out more. Instead, Roanoke is expected to become denser in the next ten years because on-site workers tend to live in urban centers, while those who work from home prefer suburban and rural areas and, sometimes, urban. Different work arrangements lead to distinct residential preferences. By including the workers' individual behaviors in the models, the model's accuracy increased from 86% to 89.78%. Logistic regression analysis highlights the factors influencing land use changes, such as proximity to roads, slopes, home values, and wages. This research helps city planners and policymakers understand working arrangement trends and create better policies to manage urban development. It shows the complex relationship between work practices and city structures, providing valuable insights for future city planning.