Driven by an explosion of data, the digital transformation of the travel sector has made Recommender Systems (RS) essential for corporate strategy and traveler decision-making. Following PRISMA 2020, this systematic literature analysis examines 70 peer-reviewed publications on Tourism Recommender Systems (TRS) from 2018 to 2024, focusing on methods, applications, assessment, challenges, trends, sustainability, and ethics. The review covers designs, development, and evaluations of RS in the travel, hotel, or tourism sectors. Key findings show that the most common TRS applications are itinerary planning (18 studies) and Point-of-Interest (POI) suggestions (19 studies), supporting informed tourist decisions. Methodologically, over 28% of systems combine multiple data signals using hybrid RS paradigms to address cold-start problems and data sparsity. Common evaluation metrics include Precision@k, Recall@k, and nDCG@k; metrics for diversity, novelty, and user satisfaction are less frequently used. The study highlights the need for fairness systems to balance stakeholder needs and prevent biased recommendations. Data sparsity, scalability, and combinatorial complexity in trip planning remain major challenges, despite TRS reducing computational costs and addressing data privacy issues. The study concludes that standardized datasets and evaluation metrics covering both quantitative and qualitative user satisfaction are needed. Future research should explore IoT and Large Language Models (LLMs) for real-time, context-aware recommendations. Sustainable RS models and robust fairness standards are critical for ethical global travel systems.