Learning curves are used to evaluate the perfor- mance of a machine learning (ML) model with respect to the amount of data used when train- ing. Curve fitting finds the unknown optimal co- efficients by minimizing the error prediction for a learning curve. This research analyzed the effect of parameter initialization on the performance of curve fitting. Our focus was on comparing the per- formance of sampling the initial parameters from 2 random distributions: uniform and normal on the curve fitting process for different parametric mod- els. Moreover, we looked into the effect of chang- ing the parameters for these 2 random distributions and drew conclusions about potential best initial guesses. Finally, we arrived at the conclusion that, after choosing parameters that maintain similar data dis- tribution, uniform and normal distribution sam- pling parameter initializations perform similarly during the curve-fitting process on learning curves. Moreover, our studies highlight the sensitivity of the Levenberg-Maquardt curve fitting method’s sensitivity to bad initial guesses.