Access to quality healthcare remains a global challenge, particularly in underserved regions where inequities persist. Machine learning (ML) has emerged as a transformative tool, offering advanced predictive capabilities and data-driven insights to address these disparities. By analysing vast datasets, ML enables healthcare systems to identify patterns, optimize resource allocation, and improve decision-making processes. These innovations are crucial in areas such as early disease detection, patient outcome prediction, and operational efficiency. This study explores the integration of ML in healthcare access, focusing on its potential to enhance equity, efficiency, and inclusivity. Through robust data analytics and visualization tools, ML models can identify underserved populations, predict future healthcare needs, and develop tailored intervention strategies. For instance, ML-powered visualizations provide real-time insights into patient demographics, disease prevalence, and resource availability, empowering healthcare providers to act proactively. Moreover, the study addresses the challenges associated with ML adoption, including data privacy concerns, algorithmic bias, and the need for regulatory compliance. Ethical considerations are paramount, ensuring that ML applications promote fairness and do not inadvertently reinforce existing inequalities. By leveraging explainable AI and fairness-aware algorithms, healthcare systems can build trust and accountability in ML-driven solutions. The findings emphasize the transformative role of ML in achieving equitable healthcare access and improving outcomes. The study concludes with recommendations for integrating ML into healthcare policy and practice, highlighting its potential to bridge gaps in underserved regions and contribute to global health equity.