Journal of Scientific Research and Reports

Mastitis continues to be among the most widespread and economically impactful disorders in dairy animals globally, causing marked reductions in milk yield, deterioration of milk quality, elevated therapeutic expenses, and early culling of affected animals. Prompt and precise detection plays a pivotal role in effective control strategies, minimizing indiscriminate antimicrobial administration and sustaining overall herd efficiency. This review presents a detailed evaluation of contemporary diagnostic innovations for mastitis identification, covering conventional, molecular, immunological, sensor-oriented, and artificial intelligence–assisted methodologies. Conventional approaches such as clinical assessment, the California Mastitis Test, somatic cell count estimation, and bacteriological culturing remain fundamental diagnostic tools, although constraints related to sensitivity, specificity, and time required for results limit their standalone effectiveness. Molecular techniques, including polymerase chain reaction, real-time PCR, loop-mediated isothermal amplification, and next-generation sequencing, enable rapid and highly precise detection of causative pathogens, thereby facilitating evidence-based therapeutic decisions. Immunodiagnostic and biomarker-oriented assays, particularly enzyme-linked immunosorbent assays targeting acute phase proteins and pro-inflammatory cytokines, support early recognition of inflammatory alterations preceding overt clinical manifestation. Technological advancements such as biosensors, lab-on-a-chip platforms, infrared thermography, and sensors integrated within automated milking systems allow continuous, real-time surveillance at the farm level. The incorporation of machine learning models with multidimensional datasets substantially improves predictive capability and advances precision dairy management practices. Although remarkable advancements have been achieved, limitations related to affordability, technical infrastructure, skilled manpower, and reliable detection of antimicrobial resistance genes remain critical concerns. Prospective developments highlight the need for cost-effective point-of-care platforms, integration of multi-omics technologies, nanotechnology-enabled detection systems, and individualized herd health management frameworks. Establishing a holistic and integrated diagnostic paradigm is indispensable for safeguarding animal health, ensuring milk quality, optimizing antimicrobial stewardship, and promoting long-term sustainability of dairy production systems.