<p>The Monte Carlo (MC) method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis, radiation shielding design, and medical physics. However, with the rapid advancement of new nuclear energy systems, the Monte Carlo method faces challenges in efficiency, accuracy, and adaptability, limiting its effectiveness in meeting modern design requirements. Overcoming technical obstacles related to high-fidelity coupling, high-resolution computation, and intelligent design is essential for using the Monte Carlo method as a reliable tool in numerical analysis for these new nuclear energy systems. To address these challenges, the Nuclear Energy and Application Laboratory (NEAL) team at the University of South China developed a multifunctional and generalized intelligent code platform called MagicMC, based on the Monte Carlo particle transport method. MagicMC is a developing tool dedicated to nuclear applications, incorporating intelligent methodologies. It consists of two primary components: a basic unit and a functional unit. The basic unit, which functions similarly to a standard Monte Carlo particle transport code, includes seven modules: geometry, source, transport, database, tally, output, and auxiliary. The functional unit builds on the basic unit by adding functional modules to address complex and diverse applications in nuclear analysis. MagicMC introduces a dynamic Monte Carlo particle transport algorithm to address time-space particle transport problems within emerging nuclear energy systems and incorporates a CPU-GPU heterogeneous parallel framework to enable high-efficiency, high-resolution simulations for large-scale computational problems. Anticipating future trends in intelligent design, MagicMC integrates several advanced features, including CAD-based geometry modeling, global variance reduction methods, multi-objective shielding optimization, high-resolution activation analysis, multi-physics coupling, and radiation therapy. In this paper, various numerical benchmarks-spanning reactor transient simulations, material activation analysis, radiation shielding optimization, and medical dosimetry analysis-are presented to validate MagicMC. The numerical results demonstrate MagicMC’s efficiency, accuracy, and reliability in these preliminary applications, underscoring its potential to support technological advancements in developing high-fidelity, high-resolution, and high-intelligence MC-based tools for advanced nuclear applications.</p>