The integration of Artificial Intelligence (AI) in meteorological data analysis has revolutionized weather forecasting, providing unprecedented accuracy and efficiency. This review paper explores the diverse applications of AI models in meteorology, focusing on their ability to handle large datasets, im-prove prediction accuracy, and offer real-time analysis. The study systematical-ly examines various AI techniques, including machine learning, deep learning, and hybrid models, highlighting their strengths, limitations, and potential im-provements. Furthermore, the paper discusses the role of AI in addressing chal-lenges such as climate change, extreme weather events, and the need for pre-cise localized forecasts. The review applies AI models to temperature prediction using historical meteorological data, finding that XGBoost achieved the highest accuracy with the lowest MSE. The study employs artificial intelligence models to forecast temperature based on historical meteorological data. It reveals that the XGBoost model provided the highest accuracy with the lowest MSE. This research emphasizes the potential for AI-driven advancements in meteorology, which are expected to enhance decision-making in sectors such as agriculture, disaster management, and energy planning.