Tomatoes are a globally significant crop, valued for their nutritional and economic contributions. However, diseases such as fungal, bacterial, and viral infections pose severe threats to tomato yield and quality, endangering food security and farmer livelihoods. Early detection is critical, yet traditional methods like manual inspection and laboratory testing are slow, labor-intensive, and error prone. Machine learning (ML) has emerged as a transformative tool, offering rapid and accurate disease prediction. Despite their potential, ML models often lack interpretability, limiting their adoption in agriculture. This study investigates and compares explainable AI (XAI) techniques to enhance the transparency of ML models for tomato disease prediction, aiming to identify methods that balance interpretability with predictive accuracy. This study presents an interpretable machine learning model for tomato disease prediction that combines deep learning with traditional interpretable methods. The hybrid approach adopted achieved 95% accuracy while maintaining transparency in decision-making through multiple interpretability techniques including LIME, SHAP values, and Grad-CAM visualizations. The model demonstrated robust performance across different tomato disease types, with the highest accuracy for Early Blight (96%) and effective detection of subtle viral patterns (89%). Performance evaluation showed significant practical improvements, including an 85% enhanced detection capability and a 73% reduction in treatment response time. The model’s interpretability was validated through quantitative metrics and expert evaluation, providing clear insights into tomato disease-specific region identification. This work establishes a balanced approach between accuracy and interpretability for practical agricultural disease detection. Future work should focus on real-time implementation and adaptation to diverse environmental conditions.