Due to the effects of climate change, there has been a drastic increase in the intensity and frequency of thunderstorms, heavy rainfall, droughts, heatwaves, and cold spells. These events not only impact human life and health but also affect agriculture, water resources, energy demand, and the overall economy. Conventional climate models are limited in their predictive ability because of the vast amount of data and the complexity of nonlinear processes. To address these challenges, artificial intelligence (AI) provides an effective solution. AI-based techniques such as machine learning (ML) and deep learning (DL) can analyse climate and atmospheric data on a large scale. These techniques are capable of identifying patterns of extreme events, trends, and hidden relationships that conventional models often overlook. With the help of satellite images, historical climate records, and real-time weather forecasts, AI enables more accurate and reliable future predictions. Beyond this, AI can also help in understanding the social and economic impacts of extreme events. For example, in regions where forecasts predict droughts or floods, AI can improve decision-making related to agricultural production and food security. AI-based models play a significant role in strengthening disaster management programs, developing timely alert systems, and supporting appropriate decision-making. However, these techniques also face challenges such as the lack of large-scale, high-quality data, issues of model transparency (the “black box” problem), and limitations in resources. Nevertheless, with effective strategies, advanced computing power, and global collaboration, AI can provide a new pathway for climate science. Therefore, it is evident that AI is a powerful tool for gaining deeper insights into extreme weather and climate events, as well as for understanding their impacts. In the future, AI-based research and policy initiatives will become stronger, helping human society to better confront the challenges of climate change.