Agriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environmental conditions, including rainfall, humidity, and temperature. In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. Today, rapid changes in environmental conditions have made it difficult for the farming community to continue doing so. The existing system aims to investigate the use of machine learning techniques in crop prediction for agriculture, where environmental conditions play a critical role. Efficient feature selection methods are employed to preprocess raw data into a computable dataset, and only relevant features are included to ensure high precision and reduce redundancies. The proposed system aims to utilize a combination of machine learning algorithms to enhance crop prediction capabilities. The system employs a feed-forward backward propagation neural network to analyze soil data captured at different times, distances, and illumination levels, enabling precise assessment of soil conditions. Additionally, the system utilizes the k-nearest neighbor's algorithm to determine suitable fertilizers for various crops, ensuring optimal nutrient supply. Furthermore, the random forest algorithm is employed to predict crop yield based on a range of factors, facilitating accurate estimations for agricultural planning and decision-making. The integrated machine learning approach enhances crop yield prediction accuracy and increases productivity.