In the rapidly evolving energy sector, effective cost allocation is critical for financial sustainability and operational efficiency. Energy corporations face challenges in accurately distributing costs across multiple business units, projects, and operational activities, leading to inefficiencies and financial discrepancies. This study presents a robust cost allocation framework leveraging SAP's advanced Enterprise Resource Planning (ERP) functionalities to enhance financial performance and transparency. The proposed framework integrates SAP’s Controlling (CO) module with key financial and operational data to enable precise cost tracking, allocation, and reporting. It utilizes Activity-Based Costing (ABC) and predictive analytics to ensure fair and efficient cost distribution across diverse energy operations, including power generation, transmission, and distribution. This methodology minimizes inaccuracies, reduces overhead costs, and improves budget forecasting. A case study approach is adopted, evaluating the implementation of the framework in a multinational energy corporation. The study assesses key performance indicators (KPIs) such as cost variance, return on investment (ROI), and financial reporting accuracy. Findings suggest that implementing the SAP-driven cost allocation framework results in a 20-30% improvement in cost traceability, a significant reduction in financial discrepancies, and enhanced regulatory compliance. Furthermore, the integration of SAP’s automation tools streamlines financial workflows, reducing manual errors and enhancing decision-making. The study also highlights challenges such as data migration complexities, user adoption resistance, and system integration issues. However, these obstacles can be mitigated through strategic change management, stakeholder engagement, and continuous SAP training programs. The research contributes to energy financial management literature by demonstrating how digital transformation, particularly SAP ERP, can optimize cost structures and improve financial performance. Future research can explore AI-driven cost allocation models within SAP to enhance real-time financial analytics.