Artificial intelligence (AI) is becoming a prominent player in various scientific domains and industries. In particular, AI technologies are making inroads in tribology, where they can assist in navigating the intricacy of patterns and uncovering correlations among numerous interacting elements and processes. Extensive research publications encompass a broad spectrum of tribology subjects, such as composite materials, drive technologies, manufacturing, surface engineering, and lubricants. Consequently, the intended applications and computational methods encompass artificial neural networks, decision trees, random forest, and rule-based learners, as well as support vector machines. Therefore, the aim of this investigation is to introduce and assess the current trends and applications of AI in tribology, providing researchers and R&D engineers with support in identifying and choosing suitable and promising approaches and techniques to solve tribological problems.