Objective: The purpose of this paper is to apply an S-curve methodology to the area of knowledge of brain drain. Methods and Analysis: Thirteen non-linear models were applied through the statistical software SigmaPlot (online version) in an accumulated data series of articles obtained in the online data base SCOPUS. Afterwards, the inflection point was calculated and such was validated through "p" and "t" values. In addition, the Durbin Watson and adjusted R square values were also calculated. Findings: After the application of the thirteen non-linear models: sigmoidal 3, 4 and 5 parameters, logistic 3 and 4 parameters, Weibull 4 and 5 parameters, Gompertz 3 and 4 parameters, Hill 3 and 4 parameters and Chapman 4 and 5 parameters, the models with the best fit were sigmoidal and logistic, which gave an inflection point in the year 2023. These models were validated through the following ranges: T value greater than 2 or less than-2, P value less than 0.005. Given that the inflection point occurs in the future, this presents a great opportunity for academics and researchers who focus the topic of brain drain to publish given the high dynamism in online databases like SCOPUS. Novelty: The S-curve methodology and technology life cycle (usually executed in subjects related to technology) was implemented in this article with the innovative approach to apply it to a specific area of knowledge. Keywords: Brain Drain, Curves, S Inflection Points, Technology Life Cycle