This article examines the integration of machine learning technologies in healthcare mobile applications, focusing on their implementation, challenges, and impact across various medical settings. The article analyzes the adoption of ML-powered healthcare solutions, exploring real-time diagnostics, patient monitoring systems, and personalized treatment optimization. The article covers technical frameworks, including core ML technologies and data processing pipelines, while addressing critical challenges in data privacy, regulatory compliance, and model interpretability. The article further evaluates implementation best practices, examining model optimization techniques and validation frameworks, culminating in a comprehensive assessment of healthcare outcomes and economic benefits. The article demonstrates the transformative potential of ML integration in improving healthcare delivery, patient care, and operational efficiency through extensive analysis of multiple healthcare facilities and patient populations.