Forecasts of agricultural production and prices are intended to be useful for farmers, governments, and agribusiness industries. Because of the special position of food production in a nation\'s security, governments have become both principal suppliers and main users of agricultural forecasts. Artificial neural networks have been demonstrated to be powerful tools for modeling and prediction, to increase their effectiveness. Crop prediction methodology is used to predict the suitable crop for a field by considering the parameters: Temperature, Rainfall, Humidity, Soil properties- ( PH, nitrogen, phosphate, potassium, organic carbon, calcium, magnesium, sulfur, manganese, copper, iron )and cyclonic patterns. One of the major concern in Crop Cost Prediction (CCP) is to manage a large database with maximum attributes. In order to avoid these difficulties, a methodology named Artificial Neural Networks (ANN) with Feed Forward Back Propagation (FFBP) scheme is employed for accurate CCP. In this paper, the cost prediction accuracy is enhanced by minimizing the Mean Squared Error (MSE) by means of forecast values. Therefore, this proposed methodology outcome shows a clear idea about crop price and the crop yield estimation.