With the rise of different technologies and supercomputers, machine learning has been introducing in the new fields for different approaches or methods within multidisciplinary field of Agri-technology. Due to restricted food production, it has become seriously difficult to meet everyone’s requirement for food products like vegetables, fruits, dairy products, etc., as the population has been rapidly growing around the globe. Furthermore, the most important requirement is that people consume healthy foods. Moreover, because of the presence of a few food handling outrages and episodes in the food region, for instance, cow-like elasticity encephalitic and dioxin in poultry, a very much-recorded recognizability framework has become a need for quality control in the natural way of life. Additionally, environmental and natural change conditions, along with the prudent water of the leaders because of water deficiency, are essential troubles after a short time. Therefore, fundamentally, the foundation of an essential shift from the current perspective of improved green capacity is required. Increased lack of food, decreasing quality, food waste, and loss, limited natural resources, and other variables all have an impact on the food system. Numerous machine learning-based strategies are used to mitigate the difficulties. Using artificial intelligence-based system would make sure an advancement in agricultural yield. In the last few decades, substantial gains have been achieved in many agricultural sectors. Because of the wide range of machine learning applications, this evaluation solely discussed statistical machine learning methods using different approaches in agriculture. The well-defined statistical machine learning techniques for specific purposes are suggested, as well as the limits of each approach. The applicability of qualitative machine learning technologies in the future is considered.