During Laser Transmission welding (LTW) of thermoplastics reinforced with natural fibers, the constituents of the composite materials affect the properties of the material which relates to the appropriate design of operating parameters for attaining good-quality welds. Material composition affects the amount of laser energy absorbed, thermal properties, and optical properties. All these attributes greatly influence the properties and quality of weld joints formed from the LTW process. Given the wide range of LTW operating parameters that are feasible for joining thermoplastics, it is important to understand the effect of each parameter on important weld attributes such as weld strength and weld width track in a specific parameter design space for a given composite material and be able to predict vital weld properties such as weld strength. In this research paper, the comparison of the predicted weld strength of the LTW of Pure Polypropylene as the transparent part joined to Polypropylene doped with 0.2wt% of carbon black and reinforced with 15wt% oak wood short fibers using an empirical model based on Response Surface Method (RSM) central composite design, Artificial Neural Network (ANN) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) model was done. The coefficient of determination (R2) was (0.90), (0.93), and (0.99) for the RSM model, ANN model, and the ANFIS model respectively. All the prediction models exhibited acceptable mean absolute error percentage and root-mean-square error. The results suggested a good performance in predicting weld strength for the specified materials in the specified parameter design space in this study. ANFIS model was found to give the best performance followed by the ANN model and lastly the RSM model.