COVID-19 restrictions significantly affected SMEs, which have faced many challenges to their sustainability within this fragile new environment. This study proposes a holistic framework of sustainable performance by interrelating factors showing robust associations to produce this effect' for achieving sustainable performance in SMEs, through integrating the Technology Organisation Environment (TOE) and Resource Based View (RBV) models, to test how sustainable green practices can process the TOE factors when affecting sustainable performance. The research focuses on SMEs with worldwide employees and involves data collected from a range of different employees belonging to four different managerial levels. The process incorporated the analysis of 669 questionnaires to test the proposed hypotheses using structural equation modeling. Findings suggest that, together, TOE factors represent crucial inputs for green practices such as green training, green performance appraisal, that, in turn, mean critical processes lead to sustainable performance (output). Additionally, the findings should also inspire SMEs to focus even more effort on internal technological and organisational factors and to encourage an eco-friendly culture that would demand stakeholders adopt a more positive environmental stance.

Machine learning (ML) and deep learning (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation and innovation in various industries by integrating AI-driven solutions. Understanding ML and DL is essential to logically analyse the applicability of ML and DL and identify their effectiveness in different areas like healthcare, finance, agriculture, manufacturing, and transportation. ML consists of supervised, unsupervised, semi-supervised, and reinforcement learning techniques. On the other hand, DL, a subfield of ML, comprising neural networks (NNs), can deal with complicated datasets in health, autonomous systems, and finance industries. This study presents a holistic view of ML and DL technologies, analysing algorithms and their application’s capacity to address real-world problems. The study investigates the real-world application areas in which ML and DL techniques are implemented. Moreover, the study highlights the latest trends and possible future avenues for research and development (R&D), which consist of developing hybrid models, generative AI, and incorporating ML and DL with the latest technologies. The study aims to provide a comprehensive view on ML and DL technologies, which can serve as a reference guide for researchers, industry professionals, practitioners, and policy makers.