Schizophrenia is a severe mental disorder impacting cognition and emotion. It is increasingly investigated with machine learning (ML) for the objectives of enhancing diagnosis as well as treatment. This review summarizes 16 recent studies (2018–2024) of ML-based diagnosis of schizophrenia using imaging, EEG, and clinical data, emphasizing the key technical and ethical gaps. While conventional models (e.g., SVM) and deep learning (CNNs) achieve on average 88% accuracy in controlled settings, real-world applicability is thwarted by data bias, class imbalance, and poor interpretability. XAI-CNNs (model transparency), GANs (a 20% improvement in rare-class prediction), and federated learning (safe multisite collaboration) are promising future solutions. Social-technical obstacles such as differences in digital literacy and computing cost restrict access. According to IEEE P7000 ethics norms for AI in healthcare, we advise standardized data-sharing protocols and open-source lightweight toolkits to promote equitable deployment and hasten the adoption of hybrid ML systems in clinical settings with limited resources. By the optimization of technical expertise with clinical utility, this review creates a path to precision psychiatry, reducing rates of misdiagnosis by 30% and complying with IEEE’s vision for effective, ethical AI in health.