In summary, the hybrid model that integrates Random Forest and Convolutional Neural Networks (CNN) for the categorization of eight prevalent wheat illnesses produces very encouraging outcomes. The model's remarkable accuracy, consistency, and balance in correctly identifying the diseases— Rust Diseases, Powdery Mildew, Fusarium Head Blight (Scab), Septoria Leaf Blotch, Tan Spot, Bacterial Leaf Streak, Take-All Root Rot, and Barley Yellow Dwarf Virus (BYDV)—is demonstrated by the precision, recall, and F1-Score metrics. Recall levels, which range from 95.42% to 95.92%, emphasize the model's capacity to recognize genuine positive cases, while precision values, which range from 95.24% to 95.95%, demonstrate the model's capacity to generate accurate positive predictions. For every illness class, the F1-Score, which represents a well-balanced trade-off between recall and precision, continuously lies in the small range of 95.62% to 95.79%. The program achieves an impressive 99% overall accuracy in automating the classification of wheat diseases. The weighted average and macro F1-Scores remain consistent at 95.67%, indicating the general dependability of the model. The model's overall performance across all classes is represented by the micro-average, which likewise achieves an impressive F1-Score of 95.67%. All of these findings demonstrate how well the hybrid model performs in terms of increasing agricultural output and guaranteeing the availability of food worldwide by offering precise and automated wheat disease classification. This model's effective performance is a major advancement in the use of cutting-edge technologies in contemporary agriculture, with broad implications for crop protection & food production.