Background: The integration of computational pathology, particularly through deep learning and machine learning algorithms, has revolutionized the field of cytology and histopathology. This systematic review aims to evaluate the current advancements, diagnostic accuracy, and potential clinical applications of artificial intelligence (AI) in the diagnosis of various cytological and histopathological specimens. Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science databases from January 2015 to December 2024. Studies focusing on the application of machine learning and deep learning models in cytological and histopathological diagnosis were included. Data on diagnostic accuracy, sensitivity, specificity, and performance metrics were extracted and analysed. Results: A total of 45 studies met the inclusion criteria. Deep learning algorithms, particularly convolutional neural networks (CNNs), demonstrated high diagnostic accuracy in detecting malignant cells in cervical cytology, breast FNAC, and histopathological slides of lung and gastrointestinal tumours. The AI models exhibited an average accuracy of 92.5%, sensitivity of 90.8%, and specificity of 93.2%. Moreover, AI-assisted diagnosis significantly reduced interobserver variability and improved diagnostic workflow efficiency. Conclusion: Computational pathology has shown promising potential in augmenting diagnostic accuracy and efficiency in cytology and histopathology. However, further large-scale, multicentre validation studies are required to ensure robustness and generalizability before widespread clinical implementation.