Biofilm-associated infections pose a persistent challenge in clinical practice, characterized by their chronic nature, resistance to conventional antimicrobial therapies, and frequent association with indwelling medical devices. Current diagnostic and treatment modalities often prove ineffective in detecting and eradicating these complex microbial communities. In recent years, artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has emerged as a promising paradigm to advance our understanding and management of these infections. This review explores the evolving landscape of AI-driven approaches, including advancements in biofilm detection, prediction of antibiotic resistance, and in silico discovery of novel antibiofilm compounds. Although initial results are encouraging, the translation of these technologies into clinical settings is hindered by limitations such as heterogeneous datasets, limited model interpretability, and regulatory issues. Addressing these barriers will require interdisciplinary collaboration to develop robust, transparent, and clinically relevant AI frameworks tailored to biofilm-related infections.