This study introduces a practical framework for enhancing Oman Air’s operational forecasting through a combination of advanced data mining techniques. The framework overcomes limitations of traditional forecasting by combining modified principal component analysis (MPCA), kinetic correlation, and gradient boosting algorithms. These improve prediction accuracy in complex datasets and provide insights into subtle trends and correlations related to operational costs. The framework provides easily interpretable information for developing practical strategies, such as route planning, implementing dynamic pricing, and reducing waste, for the purpose of improving profitability and operational efficiency. This methodology was used to analyze Oman Air’s operations, based on publicly available information. The framework captured both linear and nonlinear patterns in the data and reduced data complexity with no loss in the most impactful relationships. The model predicted a potential annual cost savings of 40 million OMR if Oman Air reduces travel volume by 10% on routes that fail to meet performance criteria, to optimize costs and focus on more profitable operations. In addition to optimizing routes, this study recommends adopting fuel-saving technologies and improving operational forecasting for long-term sustainability and profitability. This research demonstrates that data-driven decision-making can refine predictive analytics and improve operational efficiency for Oman Air and the broader airline industry.