Artificial intelligence (AI) is emerging as a promising solution to the antimicrobial resistance (AMR) crisis. AMR, a global public health threat, has led to the development of a priority list of pathogens requiring new antibiotics. The discovery of novel antibiotics is time-consuming and expensive, with only 18 approved since 2014. AI has significantly accelerated drug discovery, improving efficiency in identifying new antibiotics. This review summarizes recent marketed antibiotics, clinical candidates, and AI's role in antibacterial drug development, including small molecules, antimicrobial peptides, phage therapy, essential oils, resistance mechanism prediction, and antibiotic stewardship. AMR is a serious global threat, with rising mortality rates and significant healthcare costs. The WHO reports that AMR caused 4.95 million deaths in 2019, with 1.27 million specifically from bacterial AMR. AI technologies are increasingly integrated into drug discovery, particularly in medicine, where they have enabled the discovery of novel drugs and expedited clinical research. AI is central to tackling the AMR crisis, with applications in drug design, structure optimization, and exploration of new mechanisms of action. In clinical development, new antibiotics are being developed to address the rapid increase of antibiotic resistance. From 2014 to 2021, 18 antibiotics were approved, including one for extensively drug-resistant tuberculosis. These include various classes such as fluoroquinolones, β-lactam/β-lactamase inhibitors, glycopeptides, tetracyclines, oxazolidinones, aminoglycosides, nitroimidazoles, and others. Biologicals, including monoclonal antibodies, phage endolysins, and polyclonal antibodies, are also being developed to address the growing demand for novel antibacterial agents with new targets and mechanisms of action. Polymyxins, considered last-line antibiotics, are being optimized for efficacy and reduced toxicity. Derivatives of polymyxins are in clinical development, targeting carbapenem-resistant bacteria. SPR206, a novel polymyxin derivative, shows high antimicrobial activity and lower nephrotoxicity. QPX9003, a synthetic lipopeptide, has a wider therapeutic window and reduced nephrotoxicity compared to existing polymyxins. MRX-8, a novel polymyxin analog, has shown potent activity against multidrug-resistant Gram-negative bacteria and reduced renal toxicity. Antimicrobial peptides (AMPs) are being developed as novel antibacterial agents. AMPs, typically composed of 2–50 amino acids, are produced by multicellular organisms as a defense mechanism. AI is being used to design and optimize AMPs, with examples such as Reltecimod and Murepavadin showing promising results in clinical trials. AI-based methods are also being used to predict novel AMPs and improve their antimicrobialArtificial intelligence (AI) is emerging as a promising solution to the antimicrobial resistance (AMR) crisis. AMR, a global public health threat, has led to the development of a priority list of pathogens requiring new antibiotics. The discovery of novel antibiotics is time-consuming and expensive, with only 18 approved since 2014. AI has significantly accelerated drug discovery, improving efficiency in identifying new antibiotics. This review summarizes recent marketed antibiotics, clinical candidates, and AI's role in antibacterial drug development, including small molecules, antimicrobial peptides, phage therapy, essential oils, resistance mechanism prediction, and antibiotic stewardship. AMR is a serious global threat, with rising mortality rates and significant healthcare costs. The WHO reports that AMR caused 4.95 million deaths in 2019, with 1.27 million specifically from bacterial AMR. AI technologies are increasingly integrated into drug discovery, particularly in medicine, where they have enabled the discovery of novel drugs and expedited clinical research. AI is central to tackling the AMR crisis, with applications in drug design, structure optimization, and exploration of new mechanisms of action. In clinical development, new antibiotics are being developed to address the rapid increase of antibiotic resistance. From 2014 to 2021, 18 antibiotics were approved, including one for extensively drug-resistant tuberculosis. These include various classes such as fluoroquinolones, β-lactam/β-lactamase inhibitors, glycopeptides, tetracyclines, oxazolidinones, aminoglycosides, nitroimidazoles, and others. Biologicals, including monoclonal antibodies, phage endolysins, and polyclonal antibodies, are also being developed to address the growing demand for novel antibacterial agents with new targets and mechanisms of action. Polymyxins, considered last-line antibiotics, are being optimized for efficacy and reduced toxicity. Derivatives of polymyxins are in clinical development, targeting carbapenem-resistant bacteria. SPR206, a novel polymyxin derivative, shows high antimicrobial activity and lower nephrotoxicity. QPX9003, a synthetic lipopeptide, has a wider therapeutic window and reduced nephrotoxicity compared to existing polymyxins. MRX-8, a novel polymyxin analog, has shown potent activity against multidrug-resistant Gram-negative bacteria and reduced renal toxicity. Antimicrobial peptides (AMPs) are being developed as novel antibacterial agents. AMPs, typically composed of 2–50 amino acids, are produced by multicellular organisms as a defense mechanism. AI is being used to design and optimize AMPs, with examples such as Reltecimod and Murepavadin showing promising results in clinical trials. AI-based methods are also being used to predict novel AMPs and improve their antimicrobial