Infrastructure project finance operates at the intersection of long-lived assets, complex risk profiles, and capitalintensive funding structures. Traditional approaches to risk allocation and capital structuring have relied heavilyon contractual precedent, qualitative judgment, and static financial models, often limiting their responsiveness touncertainty, market volatility, and evolving stakeholder incentives. As infrastructure investment scales globallyand private capital plays an increasingly prominent role, there is a growing need for data-driven frameworks thatenhance both risk allocation efficiency and capital productivity. This study proposes a data-driven framework forinfrastructure project finance that integrates quantitative risk analytics, performance data, and advanced financialmodelling to support optimal allocation of construction, operational, demand, and financial risks. At a systemlevel, the framework leverages historical project data, market indicators, and scenario analysis to identify risktransfer thresholds that align incentives among sponsors, lenders, investors, and public authorities. By groundingallocation decisions in empirical evidence rather than assumptions, the approach reduces mispricing of risk andmitigates the likelihood of contingent liabilities reverting to the public sector. From a capital efficiencyperspective, the framework enables dynamic optimisation of leverage, tenor, and capital mix by linking riskadjusted cash-flow variability to funding costs and return requirements. Data-driven stress testing and probabilisticmodelling improve resilience to interest rate shifts, demand shocks, and refinancing constraints, while supportingmore efficient deployment of long-term institutional capital. Importantly, the framework also enhancestransparency and comparability across projects, strengthening governance, credit assessment, and regulatoryoversight. Overall, a data-driven approach to risk allocation and capital efficiency offers a scalable pathway toimproving financial sustainability, investor confidence, and value-for-money outcomes in infrastructure projectfinance.