Antimicrobial resistance (AMR) is a significant global public health threat, with the World Health Organization (WHO) identifying a list of priority pathogens requiring novel antibiotics. The discovery and development of new antibiotics are time-consuming and expensive, leading to a critical need for innovative solutions. Artificial intelligence (AI) has emerged as a promising tool to accelerate the discovery and development of novel antibiotics. This review summarizes the progress in the field, including the involvement of AI in the development of small molecules, antimicrobial peptides, phage therapy, and essential oils. AI technologies, such as machine learning (ML) and deep learning (DL), have significantly improved the efficiency of drug discovery by enabling compound library screening, structure prediction, and rational drug design. For instance, AI-driven methods have been used to identify novel biosynthetic gene clusters (BGCs), predict protein structures and functions, and design de novo compounds. Additionally, AI has facilitated the repurposing of existing drugs and the discovery of antimicrobial peptides (AMPs) through sequence mining and de novo design. In phage therapy, AI has played a crucial role in identifying phages, predicting phage virion proteins (PVPs), and analyzing phage lifestyles. Overall, AI is revolutionizing the antimicrobial drug development process, offering hope for addressing the growing AMR crisis.Antimicrobial resistance (AMR) is a significant global public health threat, with the World Health Organization (WHO) identifying a list of priority pathogens requiring novel antibiotics. The discovery and development of new antibiotics are time-consuming and expensive, leading to a critical need for innovative solutions. Artificial intelligence (AI) has emerged as a promising tool to accelerate the discovery and development of novel antibiotics. This review summarizes the progress in the field, including the involvement of AI in the development of small molecules, antimicrobial peptides, phage therapy, and essential oils. AI technologies, such as machine learning (ML) and deep learning (DL), have significantly improved the efficiency of drug discovery by enabling compound library screening, structure prediction, and rational drug design. For instance, AI-driven methods have been used to identify novel biosynthetic gene clusters (BGCs), predict protein structures and functions, and design de novo compounds. Additionally, AI has facilitated the repurposing of existing drugs and the discovery of antimicrobial peptides (AMPs) through sequence mining and de novo design. In phage therapy, AI has played a crucial role in identifying phages, predicting phage virion proteins (PVPs), and analyzing phage lifestyles. Overall, AI is revolutionizing the antimicrobial drug development process, offering hope for addressing the growing AMR crisis.