Bijay Mihir Kunar, S.Chandar, K.R.2026-02-042023International Journal of Mining and Mineral Engineering, 2023, 14, 2, pp. 124-1561754890Xhttps://doi.org/10.1504/IJMME.2023.133651https://idr.nitk.ac.in/handle/123456789/22100Rocks are widely used in infrastructure constructions like roads, tunnels, buildings, and dams. Understanding physico-mechanical properties of rocks is vital for selecting suitable rocks, yet some properties pose challenges in determination. High-quality core samples and precise instruments are necessary for accurate assessment. Predicting the physico-mechanical properties of rocks is a key research area in rock mechanics. Researchers have employed diverse methods, including laboratory tests, non-destructive tests, and mineralogical and petrographical characteristics, to determine rock properties. This article reviews the use of soft computing methods, artificial intelligence, and machine learning to predict rock properties through indirect methods. Indirect methods involve engineering indices tests, mineralogical and petrographical characteristics, and additional approaches such as electrical properties, crushability indices, thermal characteristics, and grinding characteristics. The article also proposes various artificial intelligence and machine learning techniques as potential future directions in prediction of rock properties. © © 2023 Inderscience Enterprises Ltd.Degrees of freedom (mechanics)ForecastingGrinding (machining)Learning algorithmsMachine learningNondestructive examinationRock mechanicsSoft computingArtificial intelligence learningIndirect methodsInfrastructure constructionMachine learning techniquesPhysicomechanical propertiesPropertyRoad tunnelRock propertiesSoft computing methodsTraditional methodRocks