Background: Brain metastases are common secondary brain tumors that create major challenges for diagnosis and treatment. The differences in their appearance and molecular features make them difficult to classify. Methods: This review followed PRISMA 2020 guidelines. Research papers published between 2015 and 2025 were searched in Pub Med, Scopus, Web of Science, IEEE Xplore, and Science Direct. Only English-language studies focused on histopathology, immunohistochemistry, molecular testing, and AI-based digital pathology were included. Results: A total of 687 studies were found, and 54 met the inclusion criteria. Traditional stains such as hematoxylin and eosin (H&E) and markers like TTF-1, CK7, CK20, GFAP, and GATA3 are still useful for identifying the origin of tumors. Newer techniques like multiplex immunohistochemistry and molecular testing provide more detailed information about tumor genetics. Artificial intelligence applied to whole-slide images improves accuracy and consistency in diagnosis. However, most AI studies are limited by small datasets and lack standardization across laboratories. Conclusions: Combining molecular testing with AI-based digital pathology can help doctors diagnose brain metastases more accurately and predict patient outcomes better. Future studies should include larger datasets, use explainable AI systems, and follow standardized laboratory methods.