The rapid proliferation of tourism data across sectors, including accommodations, cultural sites, and events, has made it increasingly challenging for travelers to identify relevant and personalized recommendations. While traditional recommender systems such as collaborative, content-based, and context-aware systems offer partial solutions, they often struggle with issues like data sparsity and overspecialization. This study proposes a novel hybrid recommender system that combines evolutionary Apriori and K-means clustering algorithms to improve recommendation accuracy and efficiency in the tourism domain. Designed specifically to address the diverse and dynamic tourism landscape in Iraq, the system provides personalized recommendations and clusters of tourist destinations tailored to user preferences and contextual information. To evaluate the systems performance, experiments were conducted on an augmented dataset representative of Iraqs tourism activity, comparing the proposed system with existing methods. Results indicate that the proposed hybrid system significantly reduces execution time by 27-56% and space consumption by 24-31%, while achieving consistently lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, thereby enhancing prediction accuracy. This approach offers a scalable, context-aware framework that is well-suited for application in regions where tourism data is limited, such as Iraq, ultimately advancing tourism recommender systems by addressing their limitations in complex and data-scarce environments.