This review highlights various experimental and mathematical modeling strategies to investigate the fate and transport of antibiotics that elucidate antimicrobial selective pressure in aquatic environments. Globally, the residual antibiotic concentrations in effluents from bulk drug manufacturing industries were 30- and 1500-fold greater than values reported in municipal and hospital effluents, respectively. The antibiotic concentration from different effluents enters the waterbodies that usually get diluted as they go downstream and undergo various abiotic and biotic reactive processes. In aquatic systems, photolysis is the predominant process for antibiotic reduction in the water matrix, while hydrolysis and sorption are frequently reported in the sediment compartment. The rate of antibiotic reduction varies widely with influencing factors such as the chemical properties of the antibiotics and hydrodynamic conditions of river streams. Among all, tetracycline was found to more unstable (log K ow = − 0.62 to − 1.12) that can readily undergo photolysis and hydrolysis; whereas macrolides were more stable (log K ow = 3.06 to 4.02) that are prone to biodegradation. The processes like photolysis, hydrolysis, and biodegradation followed first-order reaction kinetics while the sorption followed a second-order kinetics for most antibiotic classes with reaction rates occurring in the decreasing order of Fluoroquinolones and Sulphonamides. The reports from various experiments on abiotic and biotic processes serve as input parameters for an integrated mathematical modeling to predict the fate of the antibiotics in the aquatic environment. Various mathematical models viz. Fugacity level IV, RSEMM, OTIS, GREAT-ER, SWAT, QWASI, and STREAM-EU are discussed for their potential capabilities. However, these models do not account for microscale interactions of the antibiotics and microbial community under real-field conditions. Also, the seasonal variations for contaminant concentrations that exert selective pressure for antimicrobial resistance has not been accounted. Addressing these aspects collectively is the key to exploring the emergence of antimicrobial resistance. Therefore, a comprehensive model involving antimicrobial resistance parameters like fitness cost, bacterial population dynamics, conjugation transfer efficiency, etc. is required to predict the fate of antibiotics. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.