Wind power has become a central component of the transition to low‐carbon energy, being recognized as a clean and cost‐effective renewable resource. This study applies a rigorous two‐phase analysis to archival supervisory control and data acquisition (SCADA) data from a Turkish wind turbine to quantify its operational performance and improve forecasting. In the first phase, turbine output is evaluated against a contractual power purchase agreement (PPA) that mandates a daily supply of 160 MW (with a ±5–10% tolerance). We find that the turbine’s average daily output (≈185.6 MW) substantially exceeds this target. We characterize the turbine’s stable operating regimes and examine the regulatory framework for imbalance penalties, noting that deviations (under‑ or over‑generation) trigger financial penalties. In the second phase, we develop and compare state‐of‐the‐art predictive models for short‐term power output. We implement several supervised learning techniques – including Random Forests, Extreme Gradient Boosting (XGBoost), and long short‐term memory (LSTM) recurrent neural networks – as well as a novel hybrid XGBoost+LSTM architecture. These models are trained on historical SCADA measurements and relevant meteorological inputs. Consistent with recent literature, ensemble tree methods and deep neural networks achieve high accuracy in wind forecasting. The resulting forecasts inform both day‑ahead compliance scheduling and longer‑term strategic planning, enabling operators to optimize performance and minimize imbalance costs. Finally, the analysis is distilled into operational and policy recommendations. Our results underscore that advanced predictive analytics – grounded in machine learning and real‐time SCADA monitoring – are essential for maximizing wind farm output while meeting contractual obligations. This work therefore provides a technical foundation for enhancing wind power generation efficiency and avoiding penalty‐inducing forecast errors in modern energy markets.