It delved into the pyrolysis-gasification behavior of waste pharmaceutical blisters (WPBs), exploring both kinetics and thermodynamics, and employing machine learning modeling. The decomposition of WPBs occurred in three stages, with temperature intervals of 140–350, 350–500, and 500–900 °C, respectively. Cyclization/aromatization reactions were hindered, as indicated by mass spectrometric analysis, which revealed the main evolved products, including CxHy, CH3OH, CH4, and H2. Apparent activation energy values obtained from FWO, KAS, Friedman, Cai & Chen models were comparable, showing a decreasing trend ranging from 264.9 to 48.4 kJ mol−1 at α ≤ 0.70, followed by a subsequent increase to 283.1 kJ mol−1 at a conversion of 0.80. The D1 model was more reliable in describing the pyrolysis-gasification process of WPBs. Machine learning modeling results indicated that the ANN19 with a topology structure of 5 ∗ 15 ∗ 1 and genetic programming models showed superior performance in predicting TG data.