Weather forecasting involves the use of science and technology to forecast weather conditions for a particular area continues to be a major challenge worldwide. This project focuses on estimating weather conditions through predictive analysis. To achieve this, an evaluation of various data mining techniques is essential prior to implementation. This study proposes a classification-based approach for weather prediction, utilizing algorithms such as Naive Bayes and Chi-Square for classification tasks. The system is designed as a web application with an intuitive graphical user interface. Users can log in with their credentials and provide input, such as current weather parameters including outlook, temperature, humidity, and wind conditions. Based on these inputs, the system processes the data, compares it with the information stored in its database, and predicts the weather. The system incorporates two primary functions: classification (training) and prediction (testing). Results indicate that these data mining methods are effective tools for weather forecasting.