CliniGuide addresses the growing need for precise, context-aware medication recommendations in healthcare by leveraging large language models (LLMs) enhanced with Knowledge Distillation (KD) and Retrieval-Augmented Generation (RAG). Unlike generic AI systems that often lack detail and transparency, CliniGuide focuses on delivering explicit, personalized medication suggestions including dosage and medication details while ensuring interpretability aligned with clinical best practices. The system follows a two-phase framework. First, a teacher LLM generates distilled data to fine-tune a smaller, more efficient student model. This KD process preserves critical medical knowledge while optimizing performance and reducing computational demands. In the second phase, RAG is employed: the system retrieves domain-specific information from a vector store based on user input and combines it with the student model's reasoning using chain-of-thought prompt engineering. This ensures that recommendations are not only contextually relevant but also transparent and explainable. Preliminary evaluations demonstrate improved clarity and accuracy in medication suggestions. Metrics such as BLEU (0.0196), ROUGE-1 (0.2674), ROUGE-2 (0.0519), and ROUGE-L (0.1348) indicate that CliniGuide effectively captures key concepts and maintains relevance, with room for refinement in textual precision. Overall, CliniGuide represents a significant step toward AI-driven, patient-specific medication recommendation systems that are trustworthy, actionable, and clinically aligned.