Businesses require capital budgeting as their key process to choose projects which produce maximum long-term value from investment opportunities. The examination process of capital budgeting consists of assessing new investments or projects so businesses can select those most appropriate for their financial targets along with their risk capacities. The past business world executed its investment assessments through combination of Net Present Value (NPV) and Internal Rate of Return (IRR) traditional approaches. The adoption of these evaluation methods occurs widely because they simplify the process of project profitability assessment. The traditional evaluation methods face increasing pressure because rapid technological changes and rising uncertainty began to challenge their effectiveness in modern business operations.

Real Options Analysis (ROA) along with AI-driven decision models constitute new approaches that overcome traditional techniques' deficits. ROA includes an adaptive mechanism that allows companies to respond to market changes because uncertainties affect strategic industries notably. Real Options Analysis provides optimal value to businesses running risk-filled projects like those in energy and technology that face significant uncertainty (Slade, 2001). The advancement of machine learning and artificial intelligence technologies has enabled businesses to process enormous volumes of data so they can perform outcomes predictions accurately and improve their decision processes (Angelo, Ayres, & Stanfield, 2018). The implementation of AI-driven assessment tools through predictive analytics advances capital budgeting decisions by helping identify risks while finding patterns and generating scenario simulations.

The study analyzes evaluation methods for capital budgeting opportunities between traditional NPV and IRR approaches against modern ROA and AI and machine learning methods. This research evaluates the alignment between the investigated methods with organizational strategies from a sustainability and corporate long-term goal perspective. Traditional methods deliver satisfactory results when markets remain stable but prove ineffective for dealing with unpredictable market situations and including environmental and social effects in evaluations. Advanced techniques help companies perform complex decision-making which integrates factoring risk management together with uncertainty and flexibility into their strategy (Brounen, De Jong, & Koedijk, 2004).

Traditional capital budgeting methods lead investment decision processes among small businesses with predictable market conditions but industrial sectors using advanced techniques to enhance performance and mitigate risks in their high-risk business environments. The capacity to handle investment-related uncertainty differentiates companies in high-risk sectors since it produces better volatility management results. The adoption of artificial intelligence tools alongside real options models by technology combined with energy and financial industries aims at outperforming competitors and achieving more accurate investment choices. ESG (Environmental Social and Governance) elements and sustainability are transforming the way organizations perform capital budgeting analysis because companies want to link their investment decisions to societal purpose (Alles et al., 2021; Sureka et al., 2022). The changed approach enhances financial outcome decisions while supporting business sustainability during the long term.

 This research shows that traditional budgeting methods will maintain their importance but implementing cutting-edge capital budgeting systems provides organizations with market-responsive capabilities that enhance their decision-making quality as well as environmental sustainability performance. The thorough method enables companies to make better strategic and informed choices which results in enduring competitive market performance.