<p>The traditional methods of safety and quality check are effective but often entail time consuming processes and may lack sensitivity required for early detection. This warrants ways to improve pathogen detection efficiency by investigating the application of artificial intelligence (AI) in dairy quality control. The increase in milk output emphasizes the necessity of updated quality control procedures. AI solutions give farmers previously unheard-of chances to boost output, improve sustainability, and use less resources. AI is used in dairy farming for a variety of purposes, including data entry, economic analysis, and enhancing animal health. Exploring AI's historical foundations, the paper highlights AI's function as a collaborator rather than a rival in human association. It discusses several uses of AI in the dairy sector, such as milking robots, drones, and the Internet of Things (IoT). Support vector machines, machine learning, and artificial neural networks are investigated as AI approaches that improve the productivity of dairy farms. In this review, traditional techniques for identifying pathogens in milk such as PCR, ELISA, and culture-based approaches are examined. The paper also examines case studies and empirical evidence highlighting the efficacy of AI-based tools in pathogen detection across various stages of dairy production and distribution. Additionally, it discusses the regulatory landscape and potential challenges associated with the widespread adoption of AI technologies in dairy quality control. The assessment ends with future prospects, highlighting the potential for cooperation between AI developers and dairy stakeholders to enhance supply chain security, operational procedures, and business models. All things considered, the application of AI to dairy quality control shows potential for improving productivity and ensuring the security of dairy products in the constantly shifting dairy market.</p>