This chapter presents an artificial intelligence-driven framework for emission optimization in combined-cycle power plants (CCPPs). The proposed methodology integrates thermodynamic modeling, data analytics, and neural-network optimization to address the challenge of reducing nitrogen oxides (NOx) emissions while maintaining plant efficiency. Four years of high-resolution operational data from a 150 MW gas-turbine station located in north-western Türkiye were analyzed using neural designer and validated through a Python-based simulation pipeline. The hybrid framework transitions from deterministic parameter-based formulations to data-driven predictive models through a systematic sequence of more than forty design and validation steps. An online surrogate-simulation engine, trained on historical operating conditions, predicts NOx variations without executing iterative optimization for each scenario. The optimized configuration achieved a gas-turbine exhaust pressure PGTE = 17.844 mbar and an outlet NOx concentration \( {C}_{{\textrm{NO}}_x}=78.66\ \textrm{mg}\ {\textrm{m}}^{-3} \) . The model employs a quasi-Newton (BFGS) learning algorithm with L2-regularization to minimize the composite loss index, enabling high predictive stability across unseen operating regimes. Sensitivity analysis identifies turbine inlet temperature and ambient pressure as dominant variables influencing emission intensity. This study establishes an integrated, transferable framework capable of extending to hydrogen-enriched fuels, biomass co-firing, and carbon-capture configurations.