This book provides information on how to integrate modern computational tools into traditional cytology, addressing the need to analyze digital images, molecular data, proteomic data, and clinical information. Divided into 21 chapters, it discusses computational techniques, including data pre-processing, building a neural network model, and implementing the model. Further chapters cover the basics of the digitalization of cytological images, handling multiple modes of data, artificial intelligence for cell classification, and applications of computational cytology. Ethical and practical challenges, as well as the integration of artificial intelligence in cytology workflows, are also discussed. The book is supplemented with ample illustrations and tables to aid decision-making. The book serves as a practical guide for pathologists, researchers, professionals in bioinformatics, and computer scientists, helping them understand how to integrate digital and molecular data into cytology for research, diagnosis, cancer screening, personalized management, and drug discovery.