Despite growing policy interest in Artificial Intelligence (AI), comprehensive economic evaluations using systematically consolidated secondary data remain scarce in the Indian energy context. This study addresses this gap by providing a methodological contribution through exclusive secondary data analysis of AI applications in advancing India's progress toward Sustainable Development Goal 7 (SDG 7). Through systematic analysis of 32 government reports, 15 pilot project evaluations, and national energy datasets (2018-2024), we conduct a comprehensive cost-benefit analysis of AI implementations across renewable energy forecasting, smart grid management, and predictive maintenance. Analysis of Central Electricity Authority data reveals AI-driven renewable forecasting reduces grid integration costs by 18-25%, while National Smart Grid Mission data demonstrates smart grid technologies enable utilities to reduce aggregate technical and commercial losses from 21.5% to 16.2%. The analysis reveals a positive net present value of ₹1,45,000 crore (~USD 17.4 billion) for national AI implementation, with an aggregate benefit-cost ratio of 2.8:1. The study recommends integrated policy interventions focusing on public-private partnerships and standardized data governance frameworks to accelerate economically efficient AI adoption, thus informing evidence-based energy governance and positioning India to achieve its National Electricity Plan 2024 targets.