This chapter aims to assess the role of machine learning in fostering organizational resilience in the tourism and hospitality sector. It is common knowledge that machine learning radically transformed the operations of tourism and hospitality companies that have a high affinity for advanced technology adoption. Nonetheless, scholarly attention is very scant when it comes to the nexus between machine learning and organizational resilience in the tourism and hospitality sector in the post-COVID-19 epoch. A systematic literature review methodology was adopted whereby data was extracted from secondary sources. In this sense, databases like Google Scholar, Scopus, and other institutional sources were employed. Thematic analysis was used to synthesize the collected data according to emerging patterns. This study adds value to the corpus of the extant knowledge base as it established that machine learning can ensure organizational resilience through enhancement of target marketing, provision of real-time data, payment, and ticket fraud detection, automation of customer service, trend analysis, and pattern recognition, enhancement of tourist experiences, efficient allocation of resources, and personalized recommendation systems. The results of this study add value to policymaking and practice.