The food industry is a significant contributor to the world’s economy and has a significant impact on day-to-day life of a common man’s life, impacting their financial planning. Food prices can have a significant impact on food security, nutritional choices, and economic growth. Therefore, consistent and precise food pricing prediction models are essential for the government, policymakers, food manufacturers, and consumers. In this research paper, we propose a food pricing prediction model using random forest regressor algorithm, a popular and effective machine learning algorithm. The model aims to predict food prices considering multiple elements which can impact price directly or indirectly like supply and demand, economic attributes, weather patterns, and consumer behaviour. The proposed model has four main modules: first is the data gathering, then normalize or standardize the data as part preprocessing, then the feature selection, and lastly the model development. We collected data from various sources like government databases, market study reports, environmental factors and weather forecasts. In Data preparation the noises/malformed data are removed, it is structured and simplified, and prepared the data for analysis. Feature selection was carried out to Spotify the appropriate features that will have a significant impact on forecasting food prices. Finally, we developed a random forest regressor model to forecast food prices based on the features identified in previous step. We improved the model performance using various metrics like mean squared error (MSE) and coefficient of determination (R-squared). Our results show the proposed model has a better accuracy level and reliability, with an R-squared value of 0.85, indicating that 85% of the deviation in food prices can be explained by the selected features. Food businesses can use the model to better understand and predict prices as they manage their supply chains, while consumers can use it to make more educated purchasing decisions. Overall, this research paper provides an implementation approach for a food pricing forecasting model based on the random forest regressor algorithm. Such accurate and reliable food pricing predictions could improve the systemization of predictability power, thereby speeding up the growth and development of the food industries.