Bovine mastitis remains a leading cause of economic losses in dairy production worldwide, necessitating early detection methods to minimize treatment costs, prevent chronic infections, and maintain milk quality. Conventional diagnostic approaches including somatic cell counting, California Mastitis Test, and bacteriological culture are time-consuming, labor-intensive, and often detect mastitis only after clinical manifestation. Infrared thermography has emerged as a promising non-invasive technology for early mastitis detection by capturing thermal patterns associated with subclinical inflammation in udder tissue. This review examines the application of thermal imaging technologies, advanced image processing algorithms, and machine learning-based analytics for automated bovine mastitis detection in dairy herd management. We discuss infrared thermography principles, imaging protocols, and calibration procedures specific to bovine udder health monitoring. Emphasis is placed on image preprocessing techniques, region-of-interest extraction, thermal feature analysis, and classification models that enable real-time diagnostic decision-making. Field implementation studies demonstrate the potential of thermal image analytics for herd-level surveillance, integration with automated milking systems, and precision livestock farming platforms. Despite promising accuracy rates and operational feasibility, challenges including environmental variability, hardware costs, standardization of imaging protocols, and farmer adoption remain significant barriers to widespread implementation. Future developments in sensor miniaturization, cloud-based analytics, and integration with multi-modal diagnostic systems offer pathways toward scalable, cost-effective thermal imaging solutions for proactive dairy herd health management.