This research analyzes the soil properties in detail by employing the techniques of data science, taking into account variables like temperature, moisture content, and chemical composition. Its main goals are to assess crop quality, identify weed infestations in a variety of plant species. The first step of the process involves exploratory data analysis, where missing values are identified using tools such as HeatMap. Using a range of machine learning models, such as Support Vector Classifier, Random Forest, KNN, and decision trees, the system uses random forest to achieve high accuracy (99%) in crop recommendation. In order to anticipate future 5-day data, it also integrates weather forecasting skills. In addition to offering a comprehensive approach to maximize agricultural yields, the project guarantees user-friendliness by integrating an easy-to- use interface for smooth communication with the system.