Agricultural productivity is very important for making sure that people have enough food and for helping rural economies. Traditional farming methods often lead to low crop yields because decisions are not based on data and resources are not used properly. This paper presents a Cash Crop Recommendation System (CCRS) that utilises machine learning algorithms to suggest appropriate crops according to soil and environmental conditions. The suggested system combines several datasets, such as historical crop yield records, soil parameters, and climate data, to make predictions more accurate. Random Forest and Support Vector Machine are two examples of supervised learning methods that are used to classify and suggest the best crops. The system also gives fertiliser suggestions based on the levels of Nitrogen (N), Phosphorus (P), and Potassium (K) in the soil to make it more fertile.  The system is made to be easy for farmers to use and get to through a simple interface. The proposed model's goals are to boost agricultural output, lower its effect on the environment, and promote sustainable farming.