The building sector is a predominant contributor to global energy consumption and carbon dioxide emissions, with non-domestic buildings presenting unique challenges due to their operational complexity and heterogeneous profiles. While advancements in data-driven (DD) and physics-based (PB) modeling have independently progressed, a siloed approach persists, limiting scalable and robust energy performance forecasting and optimization. This paper presents a comprehensive review and, subsequently, proposes a novel integrative framework for hybrid energy modeling tailored for non-domestic buildings. We conduct a systematic analysis of DD methods—spanning statistical models, classical machine learning (ML), deep learning (DL), and ensemble techniques—and PB approaches, including simulation tools and engineering calculations. The review critically evaluates each paradigm's strengths regarding accuracy, interpretability, scalability, and data dependency, revealing a significant research gap in Africa and a need for standardized, transferable solutions. Synthesizing these insights, we introduce the Integrated Hybrid Modeling and Transfer Learning Framework (IHM-TLF). This framework architecturally couples PB and DD models through sequential calibration, surrogate-assisted optimization, and physics-informed learning pathways. It explicitly incorporates adaptive transfer learning modules and data fusion strategies to overcome pervasive data scarcity and heterogeneity challenges. Furthermore, the framework is contextualized within a policy-supportive structure, aligning technical model outputs with actionable energy efficiency measures (EEMs), retrofit planning, and benchmarking protocols. The paper delineates a detailed validation pathway for the IHM-TLF, discusses its implementation barriers, and posits its potential to significantly enhance energy resilience, reduce operational costs, and support decarbonization targets, particularly in underrepresented and rapidly developing regions. This work aims to provide researchers, building managers, and policymakers with a unified, pragmatic roadmap for advancing building energy science toward global sustainability goals.