<p>This paper presents a Python-based spectral data processing system for γ–γ coincidence spectroscopy. The system adopts a modular architecture integrating data preprocessing, peak detection, peak boundary determination, and peak quantification. For peak quantification, it implements both a direct summation method and a Gaussian–exponential tail fitting method, and incorporates the statistics-sensitive nonlinear iterative peak-clipping (SNIP) algorithm for two-dimensional background estimation. The framework was evaluated using spectra acquired with a NaI(Tl) detector system from a standard ⁶⁰Co source, a ¹³⁷Cs/⁶⁰Co mixed source, and a ¹⁵²Eu source. Activity calculations were performed for selected validated coincidence pairs based on the Shao relation. In these selected cases, the relative deviations from the reference activities were below 7.5%. The ⁶⁰Co and ¹³⁷Cs/⁶⁰Co cases showed good consistency between the two peak-quantification methods. The ¹⁵²Eu case was used as a representative complex-spectrum test, rather than as a complete pair-by-pair benchmark of the full ¹⁵²Eu spectrum. The results indicate that the proposed framework provides a useful workflow for selected γ–γ coincidence-spectrum analysis and activity-estimation tasks under the tested conditions. Further validation is required before the method can be extended to general weak-peak quantification, low-activity samples, and broad multi-nuclide identification applications.</p>