This research presents a Buddhist tourism recommendation system aimed at enhancing personalized travel experiences through image classification and tourist behavior analysis. Focusing on Buddhist landmarks in Thailand, the system uses Convolutional Neural Networks (CNNs) to classify and categorize key features of Buddhist sites, such as architectural styles and religious symbols. Recommendations are generated based on tourist preferences, gathered from sources such as social media and surveys, which reveal patterns in spiritual, historical, and cultural interests. The system architecture includes a Data Collection Layer for gathering images and user interactions, a Data Preprocessing Layer for cleaning and augmenting the data, and a Classification Layer for feature extraction. The AI Engine Layer employs hybrid filtering methods, combining collaborative and content-based filtering to provide context-aware recommendations. A Personalization Module in the Application Layer customizes the user interface to enhance engagement. The system aims to distribute tourist traffic more evenly, promote lesser-known sites, and support cultural preservation by offering tailored experiences. The dataset for image classification consists of 1,000 images of Buddhist Phra That, categorized into 11 classes, sourced from travel blogs and social media. The dataset was split into 80% for training and 20% for testing. The results show that the model performs well overall, with a high F1-score of 0.91, demonstrating its effectiveness in classifying images and delivering relevant recommendations.