Bandung municipality, home to more than 2.5 million residents, ranks as Indonesia's third most populous city and functions as the administrative center of West Java Province. The city is a notable tourist destination and has experienced significant development, highlighted by important events such as the Smart City Declaration in 2015 and the inaugural Asian African Conference in 1955. Among its 21 museums, seven are under private management and play a crucial role in preserving cultural heritage and boosting urban tourism. This study aimed to improve the management and operational efficiency of these seven private museums in Bandung. Machine learning techniques, statistical methods, and the particle swarm optimization (PSO) algorithm were utilized to improve visitor experience, lower operational costs through decarbonization, and promote environmentally sustainable practices. The research employed K-means clustering, scatter plots, and multidimensional scaling (MDS) to categorize data and pinpoint the most effective museum exploration routes. At the same time, the Orange software package facilitated the machine learning application in this study. These techniques not only contribute to the preservation of Bandung's cultural folklore, such as the stories of Sangkuriang and Lutung Kasarung, but also create a framework for urban tourism management. The findings enhance the discussion on the integration of technology in heritage and tourism by providing valuable insights for improving museum operations, reducing costs, decarbonizing, and safeguarding cultural assets. The findings carry important implications for both national and international contexts and foster the sustainable development of cultural tourism.