Background: Early disease detection is critical for improving patient outcomes, reducing healthcare costs, and enhancing disease management. Machine Learning (ML), a subset of Artificial Intelligence (AI), has emerged as a powerful tool for identifying disease patterns from large healthcare datasets. ML algorithms can analyze clinical, imaging, genomic, and wearable-device data to facilitate earlier diagnosis than conventional methods. Objective: To evaluate the applications, effectiveness, and challenges of machine learning in early disease detection across multiple healthcare domains. Methods: A cross-sectional analytical study was conducted involving 1,500 healthcare professionals, data scientists, and medical students from tertiary healthcare institutions. Data regarding awareness, adoption, effectiveness, and barriers to ML implementation were collected through structured questionnaires and healthcare system assessments. Statistical analyses included descriptive statistics, chi-square testing, and multivariate logistic regression. Results: Approximately 81.3% of respondents reported familiarity with machine learning applications in healthcare. The most common applications included cancer detection (74.8%), cardiovascular disease prediction (69.2%), diabetic complication screening (64.7%), and infectious disease surveillance (58.3%). Institutions utilizing ML-based systems reported improved diagnostic accuracy and earlier disease identification. Major barriers included data privacy concerns, limited technical expertise, and lack of infrastructure. Conclusion: Machine learning significantly enhances early disease detection through predictive analytics and intelligent data processing. Strategic investments in infrastructure, training, and ethical governance are necessary to maximize its clinical benefits.