The volatility of prices for agricultural commodities exerts a detrimental influence on the gross domestic product of many countries. Price prediction plays a crucial role in aiding the agricultural supply chain in making informed decisions aimed at mitigating and effectively managing the potential risks associated with price changes and supply chain management. While ARIMA and exponential smoothing are commonly employed in forecasting, effectively predicting price movements effectively remains challenges, particularly with extensive datasets. To address this gap, several machine learning and deep learning models have been employed in recent times to predict price series, and this study was accomplished on a publicly available dataset. The primary discovery of this study is that machine learning models demonstrate suitability in predicting commodity prices, having 99% accuracy in predicting a certain level by linear regression.