The emergence of biological big data—spanning genomics, electronic health records (EHRs), medical imaging, and wearable biosensors—has fundamentally transformed the landscape of predictive healthcare. This article examines the growing intersection of large-scale biological data analytics and clinical decision-making, with particular emphasis on disease prediction systems and patient outcome assessment. We synthesize evidence from recent computational studies and clinical applications to evaluate how machine learning, deep learning, and natural language processing methodologies are reshaping diagnostic accuracy, prognostic modeling, and care pathway optimization. Critical data management challenges, including interoperability, data privacy, algorithmic bias, and standardization, are also analyzed. Our review affirms that, while substantial progress has been made, realizing the full translational potential of big biological data analytics requires multidisciplinary collaboration, robust governance frameworks, and equitable implementation strategies.