Early diagnosis in low-resource healthcare environments is often hampered by limited access to qualified professionals and advanced diagnostic tools. In order to improve diagnostic accuracy and transparency, this study suggests an interpretable deep learning framework that combines explainability strategies like saliency maps, SHAP, and LIME with sophisticated neural network architectures. To make AI outputs understandable to clinicians with different levels of expertise, the methodology uses a multi-stage pipeline that processes sparse, diverse medical datasets and produces concise, actionable explanations for each model decision. A thorough comparative analysis shows that interpretable models and a hybrid human–AI approach perform noticeably better than traditional deep learning and clinician-only diagnoses, resulting in increases in specificity, sensitivity, and accuracy. Incorporating explanatory outputs promotes practitioner trust, supports ongoing clinical capacity-building, and speeds up knowledge transfer among less experienced staff. These results highlight the potential of interpretable deep learning systems to provide safer and more equitable patient care, fill important gaps in early disease detection, and facilitate sustainable healthcare delivery in environments with limited resources. The suggested strategy lays the groundwork for the ethical and extensive use of AI-powered diagnostics that are suited for marginalized communities.