Meteorological data forecasting is pivotal for various sectors, including earth science, agriculture, disaster management, environment monitoring, tourism, and energy. It provides insights into factors such as humidity, wind speed, temperature, pressure, and precipitation. The main problem in these applications is how to translate big data into useful insights or knowledge. Traditional forecasting methods rely on numerical prediction models, which have limitations in accurately predicting complex weather phenomena. In recent years, machine learning algorithms have gained popularity in improving the accuracy of meteorological forecasting by analyzing large volumes of data and identifying patterns that may not be captured by traditional models. ML falls within the domain of artificial intelligence (AI) which centers on leveraging data and algorithms to replicate how humans learn. With the advent of machine learning (ML), the accuracy and reliability of forecasting have significantly improved. This comparative study mainly examines the various ML techniques used for forecasting climatic data.