The adoption and convergence of International Financial Reporting Standards (IFRS) have long been central to achieving transparency, comparability, and consistency in global financial disclosures. However, manual IFRS implementation remains costly, labor-intensive, and prone to interpretation errors particularly across jurisdictions with differing regulatory capacities and market maturity. This paper explores the application of machine learning (ML) to transform the adoption of IFRS through automated classification of accounting treatments, intelligent disclosure analysis, and compliance optimization. The proposed framework leverages supervised and unsupervised ML algorithms, including decision trees, support vector machines, and neural networks, to classify financial statement items based on IFRS-compliant taxonomy. Trained on labeled datasets of audited filings and regulatory rulings, these models accurately identify required disclosure elements, flag omissions, and recommend standardized treatments aligned with IFRS principles. Natural Language Processing (NLP) is further deployed to interpret narrative sections such as management commentary and notes to the financial statements, detecting inconsistencies and enhancing semantic alignment with IFRS disclosure objectives. Cross-jurisdictional case studies from emerging and developed markets demonstrate the system's ability to accelerate IFRS transition by automating audit checks, detecting material compliance gaps, and reducing human subjectivity in interpretation. The model outputs are integrated into an explainable compliance dashboard, which provides real-time guidance to preparers and auditors while supporting alignment with national regulators and oversight bodies. The findings position machine learning as a catalyst for enhancing IFRS adherence, enabling scalable, data-driven convergence and reducing compliance friction. Ultimately, the framework promotes a future of intelligent financial governance that strengthens investor confidence and cross-border reporting integrity.