Purpose of Review <p>This review examines the transformative impact of artificial intelligence (AI) on managing bone, joint, skin, and soft tissue infections, focusing on recent advancements and barriers to clinical integration across healthcare systems worldwide.</p> Recent Findings <p>Artificial intelligence applications demonstrate superior diagnostic accuracy in detecting subtle infection patterns, including early osteomyelitis and complex soft-tissue infections. Machine learning models predict antibiotic resistance patterns with increasing precision, supporting more targeted antimicrobial therapy. Advanced imaging analysis using deep learning enhances the detection of early-stage infections in magnetic resonance imaging, computed tomography, and ultrasound studies. Implementation challenges include concerns about patient data privacy during model development, algorithmic bias arising from limited diversity in training datasets, and insufficient external validation in varied clinical settings. Successful integration also requires alignment with existing clinical workflows, clinician engagement to ensure the interpretability of algorithm outputs, and adherence to regulatory and ethical standards.</p> Summary <p>Artificial intelligence offers transformative potential for infection management, but realizing these benefits depends on pairing technological innovation with robust ethical safeguards, interdisciplinary collaboration, and supportive policy frameworks. Establishing governance structures for secure data sharing, bias monitoring, and transparent decision-making is essential. Collaboration among clinicians, data scientists, ethicists, and policymakers will foster trustworthy and equitable deployment. Artificial intelligence is already influencing infection management, and future progress hinges on integrated strategies that ensure safe, effective, and accessible care.</p>