The accurate and reproducible quantification of nanoparticles from electron microscopy images is essential in fields such as nanoscience, materials engineering, catalysis, and biomedical research. Despite the availability of high-resolution Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), analyzing particle distributions is often a labor-intensive and subjective process, particularly in the absence of standardized, user-friendly tools. To address these limitations, this work presents a dual-threshold-driven GUI tool for rapid nanoparticle quantification from electron microscopy images, developed to combine automation, transparency, and user control within a single, open-source framework. This Python-based application leverages both adaptive Gaussian thresholding and Otsu’s global thresholding, executing them in parallel and selecting the optimal segmentation route based on connected component analysis. The resulting binary image is cleaned using morphological filtering, followed by marker-controlled watershed segmentation to accurately resolve particle boundaries, including overlapping and clustered regions. A built-in GUI enables users to manually define the scale bar on the image for dimensional calibration and to verify processing outputs step-by-step through visual overlays. Final outputs include particle size distributions (in nanometers), histogram plots with optional Gaussian fitting, and tabular reports exportable in standard formats. Benchmarking was conducted against ImageJ and data from the published literature. The tool achieved a deviation of no more than 10% in mean particle size estimation while significantly reducing the average processing time per image. Reliability is further supported by reporting size distributions as mean ± standard deviation, alongside Gaussian fitting for statistical confidence. The tool is lightweight, standalone, and easily deployable across operating systems. It demonstrated high consistency across diverse SEM/TEM images, offering a practical, interpretable, and reproducible solution for nanoparticle quantification in academic and industrial environments alike.