Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck AttentionMechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck AttentionMechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on the Tomato Leaves and PlantVillage combined datasets from Kaggle and achieved 98.63% accuracy, 98.24% precision, 96.41% recall, and 97.31% F1 score, outperforming baselinemodels. Simulation tests demonstrated the model’s compatibility across devices with computational efficacy, ensuring its potential for integration into real-time mobile agricultural applications.The model’s adaptability to diverse datasets and conditions suggests that it is a versatile and high-precision instrument for disease management in agriculture, supporting sustainable agricultural practices. This offers a promising solution for crop health management and contributes to food security.