Somatic cell count (SCC) remains the gold-standard indicator of mastitis and udder health in dairy cows, with direct impacts on milk quality, animal welfare, and farm profitability. This chapter reviews the evolution of SCC monitoring from periodic laboratory testing and cow-side screening to continuous, sensor-enabled monitoring embedded in herd health management. It compares laboratory flow cytometry, the California Mastitis Test, portable point-of-care devices, in-line sensors integrated into milking systems, and emerging microfluidic, biosensor, spectroscopy, thermography, and imaging approaches. Machine learning and computer vision are presented as analytical layers that interpret SCC, sensor, image, and herd-management data rather than as stand-alone diagnostic technologies. Across methods, emphasis is placed on accuracy, validation, technology readiness, cost, usability, interoperability, and adoption conditions. The chapter concludes by highlighting future research directions and the need for robust validation and policy alignment to support sustainable, welfare-friendly, and economically viable SCC monitoring worldwide.