Transition metal molybdates are one of the most prominent materials for energy storage devices. The present investigation establishes a strong correlation between the structure and electrochemical performance of NiMoO ₄ through Density Functional Theory (DFT). Initially, the NiMoO ₄ microspheres are directly deposited on nickel foam using a hydrothermal method by tuning experimental parameters. When employed as electrode materials, the NiMoO ₄ microspheres deliver a specific capacity of 168.9 mAh g ⁻¹ at 1 A g ⁻¹ . In addition, the material retains 80% capacity over 7000 charge‐discharge cycles with 98.3% coulombic efficiency, implying its excellent stability. DFT calculations are used to determine specific capacity and potassium ion diffusion for 5 layers of [110] planes of NiMoO ₄ . The potential energy landscape is created for [110] plane using the potassium atom minimum hopping algorithm and atomic simulation environment. The DFT results clearly align with the theoretical capacity of 203 mAh g ⁻¹ close to the experimental results. A hybrid supercapacitor (HSC) is also developed with NiMoO ₄ //AC cell delivers a specific energy of 56.3 Wh kg ⁻¹ at a specific power of 421 W kg ⁻¹ with negligible capacity loss over 15 000 cycles. This investigation offers the development of battery‐type electrodes for hybrid supercapacitors using the fundamental understanding of ion‐diffusion in the materials’ structure.

This review outlines the recent advancements in nickel cobalt phosphate- and phosphide-based supercapacitors by providing their structure–property–performance relationships and employing advanced machine learning as a predictive tool.

Transition metal molybdates (AMoO₄ where A = Ni, Co, Mn, Fe, and Zn) have attracted much attention as promising electrode materials for energy storage devices due to their multi‐electron redox capability, higher electrical conductivity, good chemical and thermal stability, and stable crystal structure to get superior electrochemical performance. Transition metal molybdates and their graphene‐based composites possess multidimensional morphology for supercapacitors. The morphology‐dependent supercapacitor behavior has been reviewed in the present article. The formation mechanism of AMoO₄ nanostructures in the form of 1D, 2D, and 3D has been identified and respective supercapacitor behavior is outlined. The density functional theory based on the calculated electronic properties of AMoO₄ has been discussed. Additionally, the application of machine learning techniques in predicting and analyzing the relationships of AMoO₄ has been discussed for the first time. By leveraging ML algorithms, we identify key parameters influencing their energy storage capabilities, providing insights into the rational design of molybdate‐based composites. Integrating experimental results with ML‐driven optimization offers a novel pathway for accelerating the development of next‐generation energy storage devices. In conclusion, future perspectives and challenges have been discussed.