Sports officials around the world are facing challenges due to the unfair nature of doping practices used by unscrupulous athletes to improve their performance. This practice includes blood transfusion, intake of anabolic steroids or even hormone-based drugs like erythropoietin to increase their strength, endurance, and ultimately their performance. While direct detection and identification of erythropoietin in blood samples of athletes have proven an effective means to uncover doping, not all the cases are easily detectable, and some analyses are too costly to be carried out on every sample. This leads to a need to develop an indirect method for detecting erythropoietin in blood samples based on different blood biomarkers. In this paper, we presented a comparison of different machine learning algorithms combined with statistical analysis approaches to identify the presence of erythropoietin drug in blood samples collected at both sea level and moderate altitude. The results presented indicate that ensemble methods like random forest and X Gboost algorithms may provide an effective tool to aid anti-doping organisations in most effectively distributing scarce resources. Implementation of these methods on the samples from elite athletes may both enhance the deterrence effect of anti-doping as well as increases the likelihood of catching doped athletes.