Artificial intelligence (AI) is bringing about a major revolution in building services engineering (BSE) in Hong Kong. This paper reviews the AI applications in BSE in relation to Hong Kong, synthesising evidence from government initiatives, industry deployments, academic research and pilot projects. The scope of the analysis includes four important areas, i.e., AI-based energy optimisation (for chiller plants and HVAC control), fault detection and diagnosis (FDD) for building energy systems, predictive maintenance for building services equipment, and building management utilising digital twin. The article explores the institutional ecosystem that enables AI adoption, including the Electrical and Mechanical Services Department (EMSD), the E&M AI Lab and collaborative platforms like the Global AI Challenge for Building E&M Facilities, and reviews the empirical evidence of energy savings, operational efficiency and maintenance improvements. The key findings include the quantifiable benefits of AI applications in Hong Kong BSE, such as 5–6.1% energy savings for chiller plant optimisation, up to 40% reduction in daily energy for AI-empowered digital twin deployments, and significant maintenance efficiency improvements. However, the evidence base is still fragmented with few peer reviewed empirical studies, inconsistent evaluation methodologies and a dominance of pilot-scale deployments. Important research gaps identified in the article include the lack of longitudinal performance evaluations, standardised evaluation frameworks, and research on scalability, interoperability, and human factors impacting AI adoption. A research agenda is proposed to move the field from pilot demonstrations to system-wide integration.