This study introduces a comprehensive methodology for detecting fungal infections on tomato leaves using advanced image processing and machine learning techniques. The procedure begins with the acquisition of high-resolution tomato leaf images, which are then resized to a standard dimension to ensure consistency and computational efficiency. The methodology includes several critical preprocessing steps, such as converting RGB images to grayscale to reduce computational complexity and emphasize intensity variations, as well as using the Canny edge detection algorithm to clearly distinguish leaf edges and features. To improve feature visibility and contrast, image enhancements such as adaptive thresholding and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used. Images are segmented to isolate regions indicative of fungal infections using Gaussian Mixture Models, which can handle complex color and texture distributions effectively. Local Binary Patterns (LBP) are used for feature extraction because they are efficient at capturing textural patterns associated with leaf infections. To classify infection types and understand infection progression, a novel ensemble model, FungiDetect-Ensemble, is used, which combines the strengths of multiclass SVM models and Recurrent Neural Networks, specifically LSTM. This ensemble approach ensures a nuanced understanding of fungal infections by combining immediate classification and temporal analysis.