This article investigates how to optimise food processing parameters using machine learning methods. A significant difficulty facing the food business is the efficient and reliable manufacture of food items. It is feasible to find the best processing parameters that result in better product quality, less waste, and better resource utilisation by utilising machine learning techniques. In this study, a thorough technique for tackling this optimization challenge is presented, including data collection, preprocessing, model choice, training, and application. The optimization process also takes into account ethical issues, ensuring that societal, environmental, and food safety concerns are taken into account. The food sector may increase the efficiency and sustainability of its processing processes by combining machine learning algorithms with domain expertise.