Predicting price of any agricultural crop is very critical because there are many factors affecting on crop price such as past and current demand of crop, past and current production of the crop, past and current crop cultivation, export and import rules of the government for the crop, past and current rainfall conditions, and many more parameter effect on crop price prediction. If there is an accurate crop price prediction, then it will be helpful to farmers regarding when to harvest, when to sell, and how to manage resources, thus providing a reduction in risks and ensuring stable supply chains. Various models such as Naive Forecast, Moving Average, Simple Exponential Smoothing (SES), Holt’s Linear Trend Model, Holt-Winters Seasonal Model, ARIMA, and SARIMA are trained through the use of time series price data, and the objectives of the models were mainly subjected toward performance assessment with various parameters. This error metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) is used for performance calculation and evaluating the model. The 80, 20 proportion of the data was used as a training set, and the rest was used to validate the performance of the models. It was assumed at the beginning that the model having the smallest RMSE, MAE, MSE, and MAPE will be considered the best fit model. This research study will help researchers identify the best model for crop price prediction and provide valuable insights to farmers and input sellers in Rajkot to improve agricultural practices and make informed marketing decisions.