In an era where technological prowess intertwines with the sanctity of healthcare, this scholarly inquiry delves into the ethical and practical realms of machine learning (ML) in disease diagnosis and treatment, juxtaposing the healthcare landscapes of the United States (USA) and Africa. The study embarks on a quest to unravel the multifaceted implications of ML, navigating through a sea of global perspectives, technological evolution and cultural nuances. The advent of ML in healthcare heralds a transformative epoch, yet it is beset with challenges and ethical dilemmas. This study illuminates these aspects, offering a comparative analysis that bridges the technological chasm between the advanced infrastructures of the USA and the burgeoning potential within Africa. To dissect the ethical and practical dimensions of ML in healthcare, the study aims to synthesize a comprehensive understanding of its global integration, focusing on the contrasting yet complementary experiences of the USA and Africa. Employing a qualitative comparative analysis, the study traverses the landscape of ML applications in healthcare, scrutinizing ethical considerations, practical challenges and the impact of cultural and socioeconomic factors. It draws from a rich tapestry of academic literature, case studies and healthcare reports, ensuring a nuanced exploration. The study concludes that while ML presents a revolutionary potential in healthcare, its successful integration necessitates a harmonious balance between innovation and practicality. Recommendations include fostering collaborative efforts, enhancing education and training in ML and developing transparent AI models. This scholarly endeavor not only sheds light on the transformative potential of ML in healthcare but also underscores the critical need for an ethical and practical approach in its application. The insights gleaned offer guiding principles for future healthcare innovations, where technology and human values coalesce to improve global health outcomes.