The growing availability of online tourism data necessitates the implementation of sophisticated recommender systems (RS) to alleviate information overload. Conventional collaborative filtering (CF) approaches encounter challenges related to data sparsity and overly simplistic single-criteria evaluations. This paper introduces a Hybrid Multi-Criteria Trust-Enhanced Collaborative Filtering (HMCTeCF) algorithm, which effectively amalgamates trust networks, multi-criteria evaluations, and dynamic preference adaptation. The algorithm integrates user and item-based trust metrics alongside real-time feedback to enhance the accuracy and breadth of recommendations. Experiments conducted on TripAdvisor datasets reveal a 16–40% improvement in mean absolute error (MAE) relative to established benchmarks. A user study involving 130 participants confirms the system’s usability, achieving 89% coverage with an impressive 99.8% sparsity. This research contributes to the evolution of tourism recommender systems by addressing cold-start issues and improving transparency via map-based visualization.