Present research efforts coupled with improved experimental techniques have provided voluminous genomic data. To convert this data into useful knowledge, novel tools for phenomenological and data driven modelling approaches are needed. This need has spurred initiation of a lot of rigorous efforts and has resulted in development of robust artificial intelligence (AI) and machine learning (ML) based models. While these paradigms individually and in synergistic combinations have been employed in various bioinformatics applications, the viral biology discipline has particularly benefitted most. These methodologies can efficiently handle single dimensional sequence to higher dimensional protein structures, microarray data, image and text data, experimental data emanating from spectroscopy, etc. Our analysis deals with ML tools like support vector machines (SVM), neural networks, deep neural networks, random forest, and decision tree. Analysis and interpretations are provided along with ample illustrations of their relevance to real-life applications. AI and evolutionary computing based tools like Genetic Algorithms, Ant Colony optimization, Particle swarm optimization and their applicability to viral biology problems are also discussed. Hybrid combination of these tools with ML techniques have resulted in simultaneous selection of informative attributes and high performance classification. This hybrid methodology has been discussed in detail. In this chapter we describe the applications artificial intelligence and machine learning in virology. While there are AI has a multitude of tools, the focus would be on a specific aspect of Ai, known as evolutionary and heuristic computing. These are mainly employed as an alternative paradigm of optimization. They are mainly nature inspired algorithms. Although very simple and straightforward to use they have been deputed to solve several problems successfully in different domains of science and engineering. Machine learning on the other hand deals with a mountain of available data, recognize hidden patterns useful and interesting to upgrade it to structure and knowledge. We provide examples of the power of AI and Machine learning with the illustration of several examples from different subdomains of viral biology. We will also provide examples where the synergistic combination of AI and ML has a been found to be a very potent tool for solving several important problems in viral biology. © Springer Nature Switzerland AG 2019. All rights reserved.