The tourism sector is in a state of continual evolution, marked by a growing demand from travellers for customized and individualized experiences within smart city destinations. In response to this evolving landscape, this research introduces an innovative approach to intelligent recommendation systems for tourism services, utilizing Multi-Agent Reinforcement Learning (MARL). The proposed methodology employs a centralized critic and decentralized actor architecture to capture intricate interactions among agents, thereby generating recommendations of superior quality. Performance evaluation conducted on a real-world dataset demonstrates the method's superiority over existing approaches in terms of recommendation accuracy and diversity. Furthermore, this paper introduces a tourism service recommendation system based on MARL and assesses its efficacy using five distinct algorithms: Real, Random, DQN, DDPG, and MADDPG. Results indicate that the MADDPG algorithm surpasses other algorithms in providing reliable, efficient, and cost-effective services to tourists. MADDPG's capacity to learn and adapt to shifting user preferences and behaviours, facilitated by a centralized critic and decentralized actors learning from agent-environment interactions, enables it to adeptly navigate complex and dynamic environments. Moreover, the research delves into the implications of these findings for the tourism industry, drawing insights from feedback obtained from 400 respondents. The results reveal a high degree of user satisfaction with the optimized tourism service recommendation system in smart city destinations, consequently fostering a strong intention among users to revisit. This study represents a notable advancement in augmenting the tourism experience through sophisticated recommendation systems tailored for smart city destinations.