ABSTRACT India's most important sources of income are without a doubt the agricultural sector and its affiliated industries, which account for a sizable portion of GDP. Widespread rural areas are beneficial to countries. However, when compared to worldwide standards, agricultural output per hectare is startlingly low, which may be a factor in the high suicide incidence among India's marginal farmers. This study offers a practical and understandable crop-yield forecast technique to help these farmers. With the help of a versatile program that uses GPS to Identify the user's location and connect farmers with the proposed system. The user inputs the region and the kind of soil as data. When applied to a crop selected by the user, machine learning algorithms allow for the prediction of crop yield or the selection of the best appropriate crop list. Crop production prediction methods include Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Multivariate Linear Regression (MLR), and K-Nearest Neighbor (KNN). With 95% accuracy, Random Forest produced the greatest outcomes among all of them. In addition, the algorithm recommends when to apply fertilizer to maximum yield.