Abstract : Climate change and its associated impacts have emerged as critical global challenges, posing significant threats to both natural ecosystems and human societies. Concurrently, the frequency and severity of natural disasters, such as hurricanes, floods, wildfires, and droughts, have increased in recent years, leading to devastating consequences for communities worldwide. To address these multifaceted challenges effectively, the integration of advanced technologies like Machine Learning (ML) has become essential. This paper presents a comprehensive review of the application of Machine Learning techniques in climate change and disaster management. The primary objective is to explore how ML models and algorithms can contribute to enhancing understanding, prediction, and mitigation efforts related to climate change and natural disasters. Firstly, the paper discusses the use of Machine Learning in climate change analysis. ML models are leveraged to analyze vast amounts of climatic data, including temperature records, precipitation patterns, and atmospheric compositions, enabling researchers to identify long-term trends, detect anomalies, and forecast future climate scenarios more accurately. Additionally, ML-based climate models help in assessing the impacts of various greenhouse gas emission scenarios, aiding policymakers in formulating effective mitigation and adaptation strategies. Secondly, the paper delves into the application of Machine Learning techniques in disaster prediction and early warning systems. By analyzing historical disaster data, satellite imagery, weather patterns, and other relevant variables, ML algorithms can forecast the occurrence and severity of disasters in specific regions. These early warning systems enable authorities to issue timely alerts, evacuate vulnerable populations, and allocate resources more efficiently, potentially reducing the loss of life and property. Furthermore, the paper examines how Machine Learning facilitates disaster response and recovery efforts. ML algorithms are utilized to process real-time data from remote sensors, social media, and other sources to assess the extent of damage after a disaster. This information aids disaster response teams in prioritizing rescue operations, allocating aid, and coordinating relief efforts promptly. The review also addresses the challenges and limitations associated with the application of Machine Learning in climate change and disaster management. These challenges include data quality and availability, model interpretability, and ethical considerations related to decision-making in critical situations.

Keyword :Machine learning, Neural Networks.