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The optical properties of polycarbonate (Makrofol) samples are investigated under alpha irradiation with 2 MeV energy at various fluences ranging from of 103 to 106 alpha/cm2, following the electrochemical etching (ECE). Several microanalyses are carried out to characterize the optical, chemical, and structural properties of the samples, including field emission scanning electron microscopy (FESEM), UV-visible absorption spectroscopy, Photoluminescence (PL) spectroscopy, and BET analysis. Surface features and functionalities are further assessed by X-ray photoelectron spectroscopy (XPS) and attenuated total reflectance/Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, surface wettability is examined through contact angle measurements. Image processing of micrographs obtained from FESEM is utilized to determine track density, induced micro-hole diameter, and corresponding depth. Finally, machine learning (ML) is employed to accurately predict unknown patterns induced by the combined alpha irradiation and ECE. Specifically, k-nearest neighbors (KNN) classification with Bayesian optimization is used to identify alpha fluence levels with 90.1 % accuracy.