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Extreme weather events pose significant threats to human life, the economy, agriculture, and various other socio-economic aspects. This thesis presents a comprehensive analysis of the patterns of climate factors and their impact on the economy and human health using state-of-the-art and emerging statistical machine learning techniques. This research consists of two parts: exploring and comparing the effectiveness of statistical models with respect to climate time series forecasting and analyzing the effects on the economy and human health. The study employs a predominantly computational approach, leveraging R, Python, and Julia to demonstrate the role of statistical computing in understanding climate change and its impacts. This thesis aims to construct powerful statistical models that establish a functional relationship between climate measurements, economic indicators, and human health. Furthermore, we speculate on potential causal relationships within the data to contribute to a deeper understanding of the causes and consequences of extreme weather events. By providing insights into the complex interplay of climate factors, economy, and health, this research seeks to inform evidence-based policy decisions that help mitigate the adverse effects of extreme weather events and foster resilience in the face of dangerous climate change.