The ability to link technology investment to revenue expansion is increasingly critical for enterprises operating in digitally intensive and platform-based markets. Organizations face the challenge of demonstrating the financial impact of technology initiatives, including AI adoption, cloud infrastructure, customer-facing platforms, and logistics automation, amid complex, multi-channel revenue streams and rapid market evolution. Traditional accounting and ROI-based methods often fail to capture indirect, delayed, or non-linear effects of technology investments on revenue growth. This study examines advanced analytical methods that enable more accurate attribution of revenue expansion to technology initiatives, integrating financial, operational, and market-level data. This categorizes analytical approaches into financial-accounting, econometric, customer- and market-level, and data-driven AI-enabled methods. Financial-accounting techniques include incremental revenue analysis, contribution margin attribution, and capitalized technology cost allocation, providing a foundation for investment evaluation. Econometric and statistical methods, such as panel data regression, difference-in-differences, and instrumental variable approaches, support causal inference and control for endogeneity in technology revenue relationships. Customer- and market-level analytics, including cohort analysis, customer lifetime value modeling, and funnel-based conversion analysis, enable measurement of technology impacts on engagement, retention, and monetization. Advanced AI-based methods, such as uplift modeling, predictive forecasting, and reinforcement learning, allow enterprises to quantify dynamic, multi-channel effects of technology investments on revenue. The study also highlights scenario-based and probabilistic frameworks for evaluating uncertainty and risk in technology deployment. Finally, the integration of these methods into enterprise decision-making and capital allocation processes is examined, emphasizing alignment with strategic objectives and governance requirements. The findings underscore the importance of combining rigorous analytical techniques with real-time data integration to optimize technology investments and maximize revenue impact.