This research examines how Artificial Intelligence (AI), specifically Deep Learning (DL) and Machine Learning (ML), is being furnished to improve agricultural practices. It utilizes high-quality datasets to evaluate models such as ANN, XGBoost, SVM, and CNN, with an emphasis on agrarian yield prediction and plant disease detection. Among these, 95% is the highest accuracy achieved by CNN in detecting diseases, while an R² score of 0.93 is the best yield prediction performed by the Deep Neural Network (DNN). Data normalization and data augmentation, which are preprocessing methods, were applied to ensure effective training. The results validate that farming’s resource efficiency, accuracy, and early detection of disease improve by the use of artificial intelligence. However, problems like model transparency, inconsistent data quality, and ethical concerns must still be addressed. Overall, the study emphasizes the potential of AI to drive more sustainable and productive agricultural systems.