Machine learning has become an increasingly important approach in the research of antibiotic resistance. Researchers can use algorithms to analyze massive datasets in order to find patterns and correlations between genes related with drug resistance and the propagation of resistant microorganisms. Moreover, machine learning approaches allow to get insights into complicated data structures that conventional epidemiological methods find difficult to perceive. It can be used to identify drug-resistant genes and mutations, develop new drugs tailored to individual patients, optimize existing therapies by better predicting patient responses than traditional methods, detect infection more accurately and quickly, simplify data analysis tasks associated with clinical trials of potential treatments, and interpret results from complex diagnostics tests. The ultimate objective of machine learning in the fight against antibiotic resistance is to reduce inappropriate prescription while optimizing individualized treatment regimens. By using this sophisticated technology to antibiotic resistance monitoring efforts, scientists may be able to acquire a better understanding of how bacteria evolve and, eventually, seek to stem the flood of antibiotic-resistant infection that is sweeping the globe.