Artificial intelligence (AI) in pathology has become a revolutionary paradigm in emerging modalities for cancer detection and screening. This progress represents a major departure from conventional, often subjective and interobserver-dependent manual assessment to computational approaches that afford higher accuracy and reproducibility. Using advanced algorithms, particularly deep learning models, AI allows the automated evaluation of histopathological images to assist with the identification of malignant changes to cytology in a more precise and standardised manner. It is therefore becoming evident that AI-based tools are helping pathologists diagnose more efficiently, leading to quicker diagnoses and ultimately better patient outcomes. This scoping review discusses studies from 2020 onwards, with a focus on AI-based image analysis methods for automated pathology in the historical development of digital pathology. Today’s approaches are driven by deep learning methods, especially convolutional neural networks and their more general variants, which are used across a wide range of cancers. The review covers applications in breast, gastrointestinal, and cervical cancers across clinical tasks such as detection, classification, grading, staging, and prognostication. While experimental results indicate promising outcomes in controlled environments, substantial challenges remain to routine clinical uptake, including a lack of external validation, data heterogeneity, workflow integration challenges, and regulatory compliance issues. This review encompasses existing technical strategies applied in real-world settings, the rationale for clinical trials, validation frameworks, translational challenges, and future prospects, thereby providing a comprehensive picture of the field’s development from experimental innovation to practical clinical application.