Emerging economies face complex challenges in transitioning from fossil-based energy systems to sustainable renewable portfolios while balancing economic growth, energy security, and environmental targets. This study introduces a novel optimization framework, the Dynamic Energy Transition Optimization Algorithm (DETOA), developed to support strategic reconfiguration of energy asset portfolios under uncertain policy, market, and technological conditions. DETOA integrates dynamic programming, scenario-based simulation, and multi-objective optimization to simultaneously maximize return on investment, minimize carbon emissions, and ensure system reliability over time. The proposed model captures temporal evolution in energy demand, capital allocation constraints, and policy-driven incentives, enabling adaptive decision-making across short-, medium-, and long-term planning horizons. To validate its effectiveness, DETOA is benchmarked against five established techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), classical Genetic Algorithm (GA), and Linear Programming (LP). Results are presented using scenario-based graphical analysis, including transition pathway curves, carbon reduction trajectories, cost-benefit trade-off plots, and portfolio diversification maps under varying carbon pricing and policy regimes. Empirical findings indicate that DETOA achieves improved convergence stability, enhanced adaptability to dynamic constraints, and up to 22 percent better portfolio efficiency compared to conventional approaches. The study provides a robust, data-driven decision-support tool for policymakers and industry stakeholders, offering actionable insights into optimizing energy transitions in resource-constrained and rapidly evolving economic environments.