Purpose Accurate pre-operative diagnosis and prognostication are critical in suspected pancreatic adenocarcinoma (PDAC) and cholangiocarcinoma (CCA), where surgery carries substantial morbidity and mortality yet may not confer survival benefit for all patients. Artificial intelligence (AI) has shown potential to improve cyto- and histopathology performance in this area. This scoping review aimed to map the current evidence on AI approaches applied to digitised pre-operative PDAC/CCA tissue and to summarise reported diagnostic and prognostic performance. Methodology This study was conducted in accordance with PRISMA extension for scoping reviews. A systematic search was conducted in five databases. Articles published between 2015 and 2025 assessing AI-models on digitised pre-operative tissues of suspected PDAC or CCA were extracted. Results Of the 601 articles screened, 12 met inclusion criteria. Eight studies analyzed fine needle aspirates (FNA), two bile duct brushings (BDB) and one fine needle biopsy (FNB). Nine studies were single centre, and three multicentre. Convolutional neural networks were the predominant model architecture. Common technical strategies included augmentation, segmentation and Z-stacking. Model performance varied across studies and reporting was heterogeneous. Two studies directly compared AI performance with human interpretation. Fang et al., (2025) concluded that AI diagnostic accuracy (90.0%) exceeded intermediate (88.3%) and junior (76.7%) cytopathologists but was lower than senior cytopathologists (95.0%). Marya et al., (2024) evaluated a computer-aided detection approach in which AI highlighted regions of interest for cytopathologists; diagnostic accuracy was similar to the original cytology interpretation, while workflow efficiency was reported to improve. Conclusion Early studies suggest AI has potential to play a valuable role in pre-operative tissue analysis for suspected PDAC and CCA in clinical practice.