The integration of resistivity methodologies in agricultural sciences has emerged as an indispensable tool for optimizing soil characterization, moisture assessment, and salinity management. Electrical resistivity techniques, including Electrical Resistivity Tomography (ERT) and Electromagnetic Induction (EMI), offer profound insights into subsurface heterogeneities, facilitating data-driven precision agriculture. The application of resistivity surveys enables real-time evaluation of soil hydrodynamics, enhancing irrigation efficiency and mitigating excessive water usage. Additionally, resistivity-based mapping delineates salinity gradients, aiding in the formulation of targeted soil remediation strategies. However, the precision of these techniques is often impeded by inherent soil variability, mineralogical composition, and external environmental fluctuations. Despite the sophisticated advancements in geophysical instrumentation, challenges persist regarding cost-effectiveness, accessibility for small-scale agrarian communities, and the integration of resistivity data with contemporary remote sensing and artificial intelligence frameworks. Addressing these challenges necessitates a multidisciplinary approach that amalgamates geophysics, agronomy, and computational analytics to refine predictive modeling capabilities. This review systematically evaluates the state-of-the-art resistivity applications in agriculture, synthesizing global research advancements to delineate their efficacy and limitations. Furthermore, it underscores the imperative need for technological refinements that enhance user accessibility and operational feasibility. By bridging the existing knowledge gaps, future research endeavors must prioritize scalable and economically viable resistivity-based solutions, ensuring their practical deployment in sustainable agricultural.