This paper proposes a data-driven investment framework for private equity (PE) firms investing in renewable energy projects, drawing on analytical practices and financing innovations from the small and medium-sized enterprise (SME) sector. While SMEs have started to adopt clean energy through performance modeling, predictive analytics and flexible financial instruments, PE investment strategies have remained based on subjective evaluation and traditional financial metrics. This research bridges the gap by combining SME insights with private equity investment principles to identify scalable tools and models that can improve deal screening, ROI estimation and risk management in renewable energy. The methodology used a multi-faceted approach combining literature review, qualitative stakeholder insights and conceptual modeling. The literature review looked at academic and industry publications on SME energy financing and PE investment practices to identify the gaps and opportunities. Qualitative data was collected through interviews and focus groups with PE managers, renewable energy developers and SME owners who had implemented clean energy solutions. These insights informed the development of conceptual models adapting proven SME approaches pay-as-you-go financing and advanced ROI forecasting to the institutional investment context. The methodology prioritized practical applicability while maintaining analytical rigor through triangulation of multiple data sources. Empirical data analysis revealed the key barriers, financing preferences and analytical tools that have shaped SME investment behaviour, providing valuable lessons for institutional investors. The resulting framework is a replicable and interpretable model for evaluating renewable energy opportunities in private markets. This model addresses the main challenges in current PE practices and incorporates data-driven elements that have proven to work in SMEs. The paper concludes with practical recommendations for PE firms’ data-rich decision support systems, performance-linked financial structures and operational analytics in portfolio monitoring. And it outlines future research avenues real-time data integration, geospatial risk modeling and sector-specific investment frameworks for emerging renewable technologies.