This paper presents a systematic literature review on the prediction of unconfined compressive strength (UCS) and elastic modulus (E) with artificial intelligence (AI) models. The study categorises three essential parts: (1) a combination of physical and mechanical properties, (2) mechanical properties, and (3) physical properties as input parameters for AI models in estimating UCS and E. The review selection was based on search keywords using title-abstract, full-text, and keywords from Scopus and Web of Science online database libraries. A total of 131 peer-reviewed research articles published from 2014 to 2024 were critically reviewed to provide answers to research-related questions related to current advancements in the prediction of UCS and E with AI models. Among the AI technologies analysed, artificial neural networks (ANN) and ANN-based models stand out as the most used AI algorithms; other algorithms, including ANFIS, RF, SVM, and XGBoost model, have been used at significant levels in predicting UCS and E with high prediction accuracy of R2 greater 0.90 with minimum mean error margins. The ANN (24.7%), ANFIS (11.7%), and RF (7.6%) have been essentially employed in many research studies to predict rock strength. The study combined mechanical and physical properties with AI models at approximately 59%, and after that, mechanical properties at 23.6%. The efficiency of AI algorithms and their application is associated with the usage of data and input parameters. This review recommends future study gaps and places emphasis on integrating rock mechanics, physical laws (Mohr–Coulomb and Hoek–Brown failure criteria) and adaptive AI techniques to advance the adaptability and reliability in predicting rock strength and deformation characteristics.