Antimicrobial resistance (AMR) is a growing silent pandemic driven by multidrug-resistant infections, particularly those associated with medical devices such as dental implants, heart valves, and urinary catheters. This review addresses the urgent need for alternative antimicrobial strategies by exploring the integration of artificial intelligence (AI) in the discovery of antimicrobial peptides (AMPs) and the rational design of bioactive surfaces. We describe how AI-based models accelerate the identification and optimization of peptide candidates with potent antibiofilm activity. Moreover, we examine recent advancements in surface engineering, such as biomimetic coatings, quorum sensing inhibitors, and enzyme-based strategies, that disrupt bacterial colonization and biofilm formation. The novelty of this work lies in its unified perspective that bridges computational prediction, materials science, and microbial pathogenesis to inform the next generation of antimicrobial surfaces. By highlighting innovative AI-assisted approaches and emerging hybrid strategies, this review underscores their potential to mitigate device-associated infections and address the broader challenge of AMR in healthcare settings.