Machine learning is a crucial decision-making tool for recommendations and predictions. In today’s agriculture, maximum efficiency and efficient use of resources are essential for sustainable food production. In this study, we have performed a systematic literature review to extract the algorithms that are used in crop prediction studies. After analysis of various machine learning based studies, we performed additional research in databases using other algorithms. This study takes a new approach to crop recommendation using machine learning. The system takes into account essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) provided by the user. By using the predictive model, it can make recommendations on crops that will produce the best results based on these inputs. The solution is to harness the power of data-driven analytics to help farmers make informed decisions, ultimately making farming more profitable and supporting good resource management. This summary provides an overview of the project and demonstrates its potential to revolutionize crop selection in modern agriculture.