Against the dual backdrop of global digitalization in education and the high-quality development of vocational education, municipal higher vocational colleges, as core institutions for cultivating regional technical and skilled talents, have made their financial digital and intelligent transformation a key driver for enhancing institutional governance efficiency and optimizing fiscal resource allocation. Traditional financial models suffer from prominent data silos, low process efficiency, weak decision-support capabilities, and lagging risk prevention, making them ill-suited to the practical demands of elevating vocational education quality. Large AI models, leveraging core capabilities such as natural language understanding, multimodal data processing, autonomous reasoning and prediction, and knowledge graph construction, offer a novel technical pathway for advancing the financial systems of higher vocational colleges from informatization to digital-intelligence integration. This study focuses on municipal higher vocational colleges, systematically reviewing the current applications and research progress of large AI models in university finance both domestically and internationally. It identifies common shortcomings in the digital-intelligent financial development of municipal vocational institutions in China and proposes an integrated application framework, “data governance–scenario empowerment–industry-finance integration–risk prevention,” tailored to local institutional resources. By addressing practical disparities between domestic and international practices, the study provides theoretical insights and practical references for the intelligent transformation of financial systems in municipal higher vocational colleges, while also contributing a China case study to international research on financial digitalization in vocational education.