This review research paper aims to provide a comprehensive overview of the current state-of-the-art techniques and methodologies in the field of interpretable medicine tablet recognition. It emphasizes the importance of explainable AI models in healthcare, particularly in medicine tablet recognition, and highlights the challenges associated with this task. The paper presents various explainable AI techniques such as rule-based methods, feature importance analysis, decision trees, gradient-based methods, and model-agnostic approaches. Furthermore, the paper includes case studies and real-world applications of interpretable medicine tablet recognition systems to showcase the practical implementation and efficacy of these techniques. Evaluation metrics for assessing the performance of interpretable models in medicine tablet recognition are discussed, covering accuracy, precision, explainability metrics, robustness, and reliability. Through a comprehensive discussion and comparative analysis of the different approaches, this paper provides insights into the strengths and limitations of each technique. It also suggests future research directions to improve the interpretability and performance of medicine tablet recognition systems.