Agriculture stands as the bedrock of worldwide food production and livelihoods, serving as a crucial pillar for sustaining economies and nourishing populations globally. While it plays a vital role in ensuring global food security, agriculture grapples with the persistent challenge posed by plant pathogens. PlantPath focuses on advancing pathogen detection methodologies within the realm of agricultural technology, particularly in the domain of precision agriculture and pathogen management. Prior research has highlighted several challenges in agricultural pathogen detection, including limited dataset diversity, species-specific models, and the propensity for overfitting in deep learning architectures. The identified challenges emphasize the necessity for inventive strategies that tackle dataset diversity, generalize across various crop species, and incorporate rigorous regularization techniques to improve model performance and dependability. Introducing a pioneering solution, PlantPath, our innovative approach adopts deep learning principles for agricultural pathogen detection. It classifies images into five distinct categories: bacteria, fungi, pests, virus, and healthy plants, with approximately 8000 images per class spanning twenty crop species within our dataset. PlantPath utilizes convolutional neural networks, notably the ResNet50 architecture, for universal pathogen detection. PlantPath detects subtle patterns on affected plant leaves, allowing for precise classification according to the specific pathogen present. It achieves an impressive accuracy of 98.27% in detecting and classifying plant pathogens, surpassing existing methods in terms of time, cost, precision and universality. Compared to previous approaches reliant on manual observation or specialized equipment, PlantPath offers automated and precise identification of pathogens from images of plant leaves.