This study proposes a theoretical framework for integrating artificial intelligence into sustainable business models for urban public transport. Drawing on a literature review and two in-depth expert interviews (in data analytics and operations), this study identifies key structural challenges in the sector, including fragmented concession models, rising costs, declining demand, environmental pressures, and weak alignment with national policies, such as the National Policy for Urban Collective Public Transport. The findings indicate that artificial intelligence alone is insufficient to achieve sustainability; it must be integrated with robust enterprise architecture and adaptive strategic planning. To support this integration, the study presents a six-phase roadmap: structural diagnosis, enterprise architecture redesign, strategic planning, AI integration, training and change management, and continuous evaluation. This roadmap offers a practical and ethical pathway for digital transformation, aligning with the United Nations’ Sustainable Development Goals. Ultimately, artificial intelligence can improve efficiency, resilience, and sustainability—provided its deployment is context-sensitive and supported by institutional leadership, organizational capacity, and cross-sector collaboration.