Mathematical modelling is the activity devoted to the study of the simulation of physical phenomena by computational processes. The goal of the simulation is to predict the behaviour of some artefact within its environment. Statistics has been traditionally used to tackle problems in data science and mathematical approaches like machine learning (ML), particularly for developing concise decision models, producing quick approximations, and forecasting changing phenomena based on known samples. Mathematical modelling has helped machines to learn based on samples and improve their performance with optimal resources. Machine learning tools have developed machine intelligence and can be applied in various domains like, for the interpretation of biomedical data generated by high-throughput genomics and proteomics programmes. Indeed, as the study of biological systems becomes quantitative, mathematical analysis plays an increasingly important role. AI has the ability to influence mathematical modelling through a variety of ideas and methodologies. Knowledge-based systems and environments, in particular, can provide representations and associated problem-solving approaches for encoding domain knowledge and domain-specific strategies for a range of ill-structured model generation and outcome interpretation challenges. Advanced AI programming languages and approaches can offer high-level mechanisms for implementing numerical models and solutions, resulting in computational mechanics programmes that are cleaner, easier to create, and more versatile. Smart & Intelligent systems attract most of the researchers in the current technological progress to determine the statistical behaviour of data. This is how academicians and researchers forecast the future using data collected from experience. Machine learning is significant because it allows businesses to see trends in customer behaviour and operating patterns while also assisting in developing new goods. The intelligent system is at the heart of many of today's most successful businesses, like Facebook, Google, and Uber.

The Accepted papers will be published in the Special Issue of  Journal of Interdisciplinary Mathematics.