Weather forecasting is a complex and important aspect of atmospheric science that uses scientific principles and data analysis to predict future atmospheric conditions. This process helps provide valuable information about temperature, humidity, wind patterns, precipitation, and atmospheric pressure over time. Historically, weather forecasting heavily relied on numerical weather prediction models and observational data. Classical methods, such as numerical simulations and statistical models, formed the backbone of meteorological predictions. While these approaches have yielded valuable insights, they often faced challenges in accurately capturing the complexities of atmospheric dynamics. The methodology entails deploying diverse machine learning classifiers, trained on historical meteorological data. This training empowers the model to discern intricate patterns and relationships within the information. Incorporating K-fold cross-validation ensures the reliability and generalizability of the model, systematically validating its performance across multiple subsets of the dataset.