The Athlete Biological Passport (ABP) was introduced to complement the direct anti-doping approach by indirectly outlining the possible use of prohibited substances or methods in sports. The ABP proved its effectiveness, at least through a deterrent effect, even though the matrices used for longitudinal monitoring (urine and blood) are subject to many intrinsic (e.g., genetic) and extrinsic (e.g., environmental conditions) confounding factors. In that context, new and more specific biomarkers are currently under development to enhance both the sensitivity and the specificity of the ABP. Multiple strategies are presently being explored to improve this longitudinal monitoring, with the development of the current modules, the investigation of new strategies, or the screening of new types of doping. Nevertheless, due to the variability induced by indirect biomarkers, the consideration of confounding factors should continuously support this research. Beyond tremendous advances in analytical sensitivity, machine learning-based approaches seem inevitable to facilitate an expert interpretation of numerous biological profiles and promote anti-doping efforts. This perspective article highlights the current innovations of the Athlete Biological Passport that seem the most promising. Through different research axes, this short manuscript provides an opportunity to bring together approaches that are more widely exploited (e.g., omics strategies) and others in the early stages of investigation (e.g., artificial intelligence) seeking to develop the ABP.