The accelerating global demand for clean, efficient, and reliable energy has positioned nanomaterial-based hybrid energy systems as one of the most promising frontiers in advanced power engineering. By exploiting the extraordinary surface-to-volume ratios, quantum confinement effects, tunable electronic structures, and superior electrochemical properties of engineered nanomaterials, hybrid energy architectures combining photovoltaic generation, supercapacitor storage, fuel cell conversion, and thermoelectric harvesting can achieve performance characteristics fundamentally inaccessible to conventional macro-scale energy systems. This paper presents a comprehensive review and advanced modeling analysis of nanomaterial-based hybrid energy systems designed for high-efficiency power applications, integrating materials science, electrochemistry, thermodynamics, and computational modeling perspectives. The study examines the role of carbon nanotubes, graphene derivatives, metal-organic frameworks, quantum dots, transition metal dichalcogenides, and perovskite nanostructures as active components in hybrid energy architectures, analyzing their contributions to charge transport enhancement, catalytic activity, interfacial energy management, and thermal stability. Advanced modeling approaches including density functional theory, molecular dynamics simulation, equivalent circuit modeling, multi-physics finite element analysis, and machine learning-assisted materials discovery are evaluated for their capacity to predict, optimize, and guide experimental development of nanomaterial hybrid systems. The paper further examines energy management strategies, power conditioning architectures, and system-level integration challenges that must be resolved for laboratory-scale nanomaterial performance gains to translate into deployable high-efficiency power systems. Findings demonstrate that appropriately designed nanomaterial hybrid architectures can achieve system efficiencies exceeding 45% under optimized conditions, with modeling-guided material selection providing pathway acceleration of 60–70% compared to purely empirical development approaches. The study concludes with an integrated framework for nanomaterial hybrid energy system design that links materials modeling, device architecture, and system integration across multiple length and time scales.