Plant diseases are among the most important dangers to agricultural output and global food security. Machine learning and automated deep plant disease classification and detection learning methods can reduce crop loss and improve precision agriculture. This research discusses some algorithms like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbor (KNN), for multi-class plant disease classification on a large-scale dataset with 70,295 training images and 17,572 validation images in 38 classes. The CNN model was trained with the Adam optimizer at a learning rate of 0.0001 and categorical cross-entropy loss after 10 epochs. It also achieved a training accuracy of 99.1% and a validation accuracy of 96.4%, which demonstrates better generalization and performance. Comparative analysis confirmed that LSTM stood at 93.43%, SVM at 65.45%, Random Forest 55.40%, and KNN 42.70%. The CNN model outperformed the other approaches, showing to be most effective for plant disease classification. An accurate evaluation with precision, recall, F1-score, and confusion matrix also confirmed the model's reliability. The CNN model trained is preserved for future agricultural use applications, with a practical and scalable solution for real-time detection of plant diseases. This research points out the ability of deep learning to transform modern agriculture by facilitating precise and automated disease diagnosis.