Releasing human pharmaceuticals to the environment is an emerging ecotoxicological concern. In this study, we examine the feasibility of evaluating the algal chronic toxicity of human pharmaceuticals using quantitative structure–activity relationship (QSAR) models and a category approach. We constructed an ecotoxicology database of human pharmaceuticals using publicly available information, such as regulatory agency reports and scientific papers. We created an algal chronic toxicity dataset using this database, and predicted the No Observed Effect Concentrations (NOEC) of human pharmaceuticals using ECOlogical Structure-Activity Relationship (ECOSAR) and KAshinhou Tool for Ecotoxicity (KATE) QSAR models. Almost half of query substances were applicable to the QSAR models, and the feasibility was confirmed with high concordant predictions—predicted/measured ratios were in the range of 0.01–100 in 92.9% and 79.1% of applicable substances in ECOSAR and KATE, respectively—and false predictions (predicted/measured ratios > 100) that could lead to significant underestimation of toxicity were rarely observed. Two case studies of diphenhydramine and lamotrigine demonstrated that detailed evaluation of target and reference substances in the corresponding chemical class could increase the reliability and accuracy of prediction results of KATE. Grouping of substances based on pharmacology revealed some category classes with a toxicological concern. Finally, a workflow model to assess algal toxicity of human pharmaceuticals was proposed based on these evaluations including QSAR predictions and category approach.