According to the Centre for Research on the Epidemiology of Disasters, the global average number of CID has tripled in less than four decades (from approximately 1,300 Climate-Induced Disasters (CID) between 1975 and 1984 to around 3,900 between 2005 and 2014). In addition, around 1 million deaths and $1.7 trillion damage costs were attributed to CID since 2000, with around $210 billion incurred only in 2020. Consequently, the World Economic Forum identified extreme weather as the top ranked global risk in terms of likelihood and among the top five risks in terms of impact in the last 4 years. These risks are not expected to diminish as: i) the number of CID is anticipated to double during the next 13 years; ii) the annual fatalities due to CID are expected to increase by 250,000 deaths in the next decade; and iii) the annual CID damage costs are expected to increase by around 20% in 2040 compared to those realized in 2020. Given the anticipated increase in CID frequency, the intensification of CID impacts, the rapid growth in the world’s population, and the fact that two thirds of such population will be officially living in urban areas by 2050, it has recently become extremely crucial to enhance both community and city resilience under CID. Resilience, in that context, refers to the ability of a system to bounce back, recover or adapt in the face of adverse events. This is considered a very farfetched goal given both the extreme unpredictability of the frequency and impacts of CID and the complex behavior of cities that stems from the interconnectivity of their comprising infrastructure systems. With the emergence of data-driven machine learning which assumes that models can be trained using historical data and accordingly, can efficiently learn to predict different complex features, developing robust models that can predict the frequency and impacts of CID became more conceivable. Through employing data analytics and machine learning techniques, this work aims at enhancing city resilience by predicting both the occurrence and expected impacts of climate-induced disasters on urban areas. The first part of this dissertation presents a critical review of the research work pertaining to resilience of critical infrastructure systems. Meta-research is employed through topic modelling, to quantitatively uncover related latent topics in the field. The second part aims at predicting the occurrence of CID by developing a framework that links different climate change indices to historical disaster records. In the third part of this work, a framework is developed for predicting the performance of critical infrastructure systems under CID. Finally, the aim of the fourth part of this dissertation is to develop a systematic data-driven framework for the prediction of CID property damages. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience. / Thesis / Doctor of Philosophy (PhD)