Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems.