Travel planning is getting more difficult with the overwhelming number of options, diverse user preferences, and the need to adapt in real time. Traditional recommendation systems, however, usually suffer from the sparsity of data, the cold-start problem, and a reliance on static data, and hence cannot respond dynamically and make personalized itinerary recommendations. It covers different recommendation approaches, such as collaborative filtering, sentiment-based methods, GPS-based systems, and social media integration, analyzing the pros and cons of each approach. Collaborative filtering personalizes recommendations but is unable to deal with sparse data. Hybrid models combining sentiment analysis and content-based filtering enhance user engagement but do not adapt in real time. GPS-based approaches improve location-based recommendations but fail to utilize the user-generated content fully. Social media data has the ability to provide information regarding user preferences, but raises serious privacy issues. Multi-day itinerary frameworks and real-time feedback mechanisms allow for dynamic recommendations but consume a lot of computational resources. Moreover, contextual factors such as weather, traffic, and cultural diversity are very less integrated, which further lowers the efficiency of the current systems. The overall solution requires the integration of a comprehensive approach which includes real-time adaptability, contextual awareness, and balance between personalization and privacy. The work presents the insights findings from this literature review on the basis for designing next-generation smart tourism technologies that effectively cater to evolving traveler needs and presents a empirical insights for the design of a robust and scalable recommendation system enhancing the travel experience through intelligent, user-centric and context-aware travel recommendation systems.