Mastitis remains one of the most prevalent and economically significant diseases affecting dairy cattle worldwide, resulting in substantial reductions in milk production, deterioration of milk quality, increased veterinary expenditures, premature culling, and diminished animal welfare. Although conventional diagnostic techniques such as somatic cell count analysis, bacterial culture, and clinical examination remain widely adopted, these approaches often identify the disease after physiological damage has already occurred. The increasing availability of precision livestock farming technologies, automatic milking systems, wearable sensors, and continuous physiological monitoring has created unprecedented opportunities for developing intelligent predictive systems capable of identifying mastitis before the appearance of obvious clinical symptoms. Recent advances in artificial intelligence have demonstrated considerable success in disease prediction through machine learning algorithms; however, existing systems frequently rely on isolated predictive models that inadequately exploit multidimensional relationships among cows, environmental variables, production characteristics, and historical disease patterns. This study proposes a predictive analytics architecture integrating machine learning and collaborative filtering to improve the accuracy and reliability of mastitis detection in dairy cows. The proposed conceptual architecture combines heterogeneous sensor data, historical herd records, behavioral monitoring, milk quality parameters, and collaborative similarity learning to generate individualized disease-risk assessments. Machine learning algorithms perform nonlinear pattern recognition, while collaborative filtering captures latent similarities among animals exhibiting comparable physiological and production characteristics. Together, these complementary mechanisms enhance prediction robustness and reduce false-positive and false-negative classifications. The paper synthesizes recent developments in machine learning, automated milking systems, risk assessment methodologies, ensemble learning, deep learning, and sensor-based monitoring exclusively from the provided literature. A comprehensive methodological framework is developed to explain data acquisition, preprocessing, feature engineering, collaborative similarity modeling, predictive analytics, validation strategies, and deployment considerations. The study further evaluates the theoretical advantages, implementation challenges, and practical implications of integrating collaborative intelligence into livestock disease prediction. The proposed architecture contributes a generalized decision-support framework capable of supporting veterinarians, dairy producers, and precision livestock management systems in achieving earlier intervention, improved herd health, optimized resource utilization, and enhanced sustainability within modern dairy farming.