Health recommendation systems utilize various algorithms to provide personalized and relevant health recommendations based on user health constraints and health problems. These algorithms include collaborative filtering, content-based filtering, hybrid recommender systems, matrix factorization, deep learning, neural networks, association rule mining, reinforcement learning, natural language processing (NLP), clustering algorithms, and time-series analysis. However, research gaps remain to improve efficiency and usefulness. These gaps include personalization for specific health conditions, long-term behavior change and adherence, integration of real-time health data, uncertainty, explainability, user trust, privacy concerns, data inequality, and bias. To address these issues, research is needed to develop transparent and comprehensible algorithms, address data inequalities and biases, and integrate diverse data types. Evaluating the impact of health recommendations on user health outcomes is crucial, and context-aware recommendations are needed for more relevant and efficient recommendations. A hybrid framework for health recommendation systems combines multiple algorithms and data sources to provide accurate and personalized health recommendations. Collaboration with healthcare experts, privacy regulations, and continuous learning from user feedback are essential steps towards a future where personalized, evidence-based, and actionable health recommendations are provided.