Recent years have seen a surge in interest in crop recommendation systems that consider the market and the weather to help farmers choose the right crops. The best crop can be predicted using machine learning approaches and algorithms based on market data, demand, and supply, and this survey article gives an overview of the available research in this area. The survey emphasises how crop recommendation systems use well-known techniques like Random Forests, Support Vector Machines, and Artificial Neural Networks. It covers its use in emerging market analysis, supply-demand information, and climatic factors to give farmers precise advice. Also mentioned are the drawbacks of the research papers under review, such as the lack of data, the narrow geographic reach, and the development of new technologies. The survey’s results highlight how machine-learning approaches can boost agricultural output and profitability while taking market dynamics and climatic conditions into account. Future research could address the survey’s limitations to create more reliable and useful crop recommendation systems that are adapted to various agricultural environments.