The global accounting profession is undergoing a structural transformation driven by workforce shortages, increasing regulatory scrutiny, and the growing complexity of financial reporting under frameworks such as IFRS and GAAP. Traditional training models, characterized by periodic certification and static curricula, are increasingly inadequate for maintaining real-time compliance and technical proficiency. At a broader level, artificial intelligence (AI) offers a paradigm shift in professional training by enabling scalable, data-driven learning ecosystems capable of continuous skill assessment and targeted knowledge delivery. This study investigates the deployment of AI-driven training platforms designed to mitigate accounting workforce shortages while strengthening global financial reporting compliance. The proposed approach integrates machine learning algorithms for competency gap detection, natural language processing for automated interpretation of regulatory updates, and adaptive learning engines that personalize training pathways based on user performance. At a more granular level, the system embeds real-time compliance validation within simulated financial reporting tasks, enabling practitioners to align outputs with standards such as Sarbanes-Oxley Act. The framework also incorporates performance analytics to track error reduction, reporting accuracy, and audit readiness. By aligning workforce development with compliance automation, AI-driven platforms provide a sustainable solution for enhancing reporting quality and institutional resilience.