In contemporary healthcare, the integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized medical diagnosis, offering unprecedented accuracy and efficiency. However, the black-box nature of many ML and AI models raises concerns regarding their interpretability and transparency, crucial aspects in medical decision-making. This paper proposes a novel framework aimed at enhancing the interpretability and transparency of ML and AI-based healthcare models in medical diagnosis. Through a comprehensive analysis of existing methodologies and techniques, this research delineates strategies to elucidate the decision-making process of these models, thus empowering healthcare practitioners with insights into the underlying rationale behind diagnostic outcomes. By employing techniques such as feature importance analysis, model visualization, and explanation generation, the proposed framework facilitates a deeper understanding of model predictions, fostering trust and confidence among clinicians and patients. Moreover, this paper explores the ethical and regulatory implications surrounding the implementation of interpretable and transparent ML/AI models in healthcare settings, emphasizing the importance of accountability and patient-centricity. Through empirical validation and case studies, the efficacy and practicality of the proposed framework are demonstrated, showcasing its potential to enhance diagnostic accuracy while ensuring transparency and accountability in medical decision-making processes. Ultimately, this research contributes to the ongoing discourse on the responsible integration of ML and AI in healthcare, advocating for models that prioritize interpretability and transparency to uphold patient safety and trust.