This study examined the cyclic behavior of glass-fiber-reinforced polymer composite column-beam connectors in various diameters. Mean learning (XGBoost and the random forest algorithm) and numerical methods were used to identify the data collected experimentally due to the rotation behavior. The initial sample had a column length of 1200 mm and a cross-section of 80 × 80 mm, with a beam section measuring 80 × 160 mm. The second sample had a 100 × 100 mm column and a beam section measuring 100 × 200 and 1200 mm, respectively. For the third sample, the column’s dimensions were 130 × 130 mm in cross-section and 1200 mm in length, and the beam was 130 × 260 mm. Spruce wood is used to make glulam components for columns and beams. Following the creation of the column-beam connections, glass-based fiber-reinforces polymer (FRP) fabric was used to wrap the column-beam connections that needed strengthening. The cyclic behavior of the reinforced reference samples and the samples with reinforced column-beam joint areas was examined. The column-beam connection with code C13B13-R has the most load-carrying capacity (13.9 kN), while the beam with code C08B08-UR has the lowest load-carrying capacity (03.4 kN). Examining the table reveals that the findings of the numerical analysis and the experiment provide comparable conclusions. When comparing experimental and numerical results, similar values are found for stiffness values (R2 of 0.99), load-bearing capacity (R2 of 0.99), and energy consumption capacity (R2 of 0.99). With R2 of 0.9978, RMSE of 0.01017, and MAE of 0.01043, the random forest approach identified the stiffness values in the model with the best accuracy. It has been shown that the seismic behavior of column-beam couplings built with these materials may be studied with mean learning models and numerical analysis, instead of demanding and time-consuming experimental studies.