Abstract Background: Patient–ventilator asynchrony (PVA) is a common complication in mechanically ventilated patients, leading to increased morbidity, prolonged ventilation, and respiratory muscle fatigue. Early detection is critical. Artificial intelligence (AI) systems, including convolutional neural networks (CNNs) and machine learning algorithms, have been applied to automate detection. Objective: To systematically review current evidence on AI-based systems for detecting PVA and analyze implications for physical therapy interventions. Methods: A systematic literature search was conducted in PubMed, Scopus, and IEEE databases from 2020 to 2025. Keywords included 'Artificial Intelligence,' 'Convolutional Neural Network,' 'Patient–Ventilator Asynchrony,' and 'Mechanical Ventilation.' Inclusion criteria were studies using AI for PVA detection in adult ICU patients. Exclusion criteria were pediatric/animal studies and non-AI detection methods. Results: Eight studies met inclusion criteria. AI algorithms, particularly CNNs and LSTM networks, demonstrated high accuracy (92–96%) in detecting various asynchrony types, including double triggering, ineffective efforts, and trigger delay. Real-time detection allowed early intervention. Conclusion: AI systems significantly enhance early detection of PVA. Physical therapists can utilize these early alerts to implement positioning strategies, respiratory muscle facilitation, and breathing exercises, potentially reducing ventilator dependency and improving outcomes.