In an evolving landscape shaped by Industry 4.0 and the emerging paradigms of Industry 5.0, the importance of resilience in supply chains should be emphasised. Resilience is the ability to avoid and anticipate disruptive events and, when their occurrence is certain, the capacity of recovering normal supply chain operation. Data science can play a crucial role in enhancing this resilience. Based on this, the main objective of this article is to conduct a meta-review on enhancing resilience in supply chains 4.0 and 5.0, focusing on data science-based approaches to offer a comprehensive overview and high-level synthesis of the current state of knowledge. Our research has shown that the majority of studies employ broad criteria for publication classification and analysis, concentrating on factors such as publication years, academic disciplines, journals, geographical distribution, and research types. However, our approach takes a more specific methodology, by emphasising context, intervention, mechanism, and outcome elements. While the prevailing focus of existing literature is on Industry 4.0-based supply chain contexts, with limited attention to Industry 5.0, the most analysed technologies include blockchain, industrial internet of things, internet of things, cloud computing, digital twins, among others. Notably, resilience enhancement in the reviewed studies predominantly relies on artificial intelligence, machine learning, data analytics, big data, and, to a lesser extent, deep reinforcement learning and predictive analysis.