Potato cultivation is crucial to the world of agriculture, but diseases remain a major threat for both yield and quality. This proposed work will focus on a new approach that integrates convolutional neural networks with other advanced algorithms like Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) for the purpose of potato leaf disease detection. The dataset is divided into training, validation, and testing segments. Data-wise, there is 80% of the training set and 10% each for the validation and testing sets given in the data. The total files available in the dataset are 13,222, including samples of all three classes: healthy, late blight, and early blight. The source of the files is published work and a few SRM Institute of Science and Technology Agriculture field files. After running 25 epochs, the system can achieve up to an accuracy of 97.01%. Through the analysis of leaf images, it can detect the presence of diseases and even evaluate the level of confidence in their respective classifications. Integration with SVM, Random Forest, or even GBM enhances accuracy. SVM particularly excels at finding the optimal separation between classes. It uses ensembles of decision trees in the case of random forest; GBM, in turn, combines weak models sequentially to make relatively better predictions. Accuracy, recall, and F1-score for SVM, random forest, and GBM are calculated, respectively. This integrated approach joins deep learning with advanced algorithms in a manner to improve the exactness of disease diagnosis and possible cultivation of potatoes in a stable way on the global level.