We present a Python-based tool for simulating and fitting Galactic Rotation Curves (GRCs) using a modular framework of physically motivated mass models. This tool directly analyzes raw observational data—either from tabulated sources or raw FITS velocity maps—by modeling contributions from the bulge, disk, and dark matter halo using Hernquist, de Vaucouleurs, exponential disk, Navarro-Frenk-White (NFW), and Burkert profiles. It supports three fitting techniques: non-linear least squares, bootstrap resampling, and Markov Chain Monte Carlo (MCMC) with full uncertainty propagation. The tool outputs best-fit parameters, confidence intervals, and a suite of diagnostic plots including rotation curve fits, residuals, and posterior distributions. Designed to address long-standing challenges in GRC analysis—such as rigid model architectures, poor error handling, and lack of component-wise transparency—our tool enables accurate, reproducible mass decomposition across diverse galaxy types. Its flexibility, efficiency, and user-friendly interface make it suitable for both pedagogical and research applications in galactic dynamics and dark matter studies.