The primary objective of this work is to use machine learning approaches to classify different jute plant diseases. We pay particular emphasis to the comparative study of several models and the development of a hybrid architecture that blends traditional machine learning methods with deep learning. The collection, which includes high-resolution images of jute plant leaves, was meticulously collected and balanced to ensure equal representation of the healthy and ill groups. Using feature extraction techniques, the unique characteristics of jute plant leaves—such as color, texture, and shape attributes—were recorded. The experiment included four popular machine learning models: XGBoost, Random Forest, Support Vector Machine (SVM), and Logistic Regression. Furthermore, a hybrid architecture including EfficientNet as a feature extractor and SVM as the classifier was applied. Each model was evaluated using metrics such as F1-score, recall, accuracy, and precision. The findings showed that XGBoost was the top-performing model, followed by Random Forest, SVM, and Logistic Regression in that order. With an accuracy of 91.75%, the hybrid design performed competitively. This study illustrates the general effectiveness of machine learning models in detecting jute plant diseases, with XGBoost showing superior performance.