The goal of this research is to use artificial intelligence computations to create a clever farming yield proposal framework. The proposed framework considers yield efficiency and the winning season to develop suitable harvest concepts. A sizable dataset of verifiable horticultural data with a variety of characteristics, including geological variables, environmental factors, and soil qualities, was utilized to prepare and assess our four widely used models, which include Linear Regression (LR), Multi-Layer Perceptron (MLP), and others. The data was further separated into portions based on irregular examples to offer crop suggestions relevant to time periods. Standard measurements were used to assess the models’ exhibition, and an outfit strategy was used to strengthen the framework. In the end, the created framework gives ranchers and rural specialists a useful tool for making informed choices, enhancing crop selection, and raising horticulture productivity as a whole.