Schizophrenia is a complex and debilitating mental disorder characterized by a range of symptoms, including hallucinations, delusions, and cognitive impairment. Early detection & prediction of schizophrenia is very important for stabilizing the condition. Normal machine learning approaches are facing few limitations like maintaining huge psychiatric data. In the present work, quantum machine learning (QML) approach for predicting schizophrenia is proposed. Effective usage of quantum computing, QML offers novel approaches to analyze complex datasets, potentially overcoming the limitations of classical machine learning algorithms. We present a comprehensive framework for schizophrenia prediction using QML, encompassing data preprocessing, feature extraction, model development, and evaluation. Key components of our approach include the utilization of quantum algorithms such as quantum support vector machines, quantum neural networks, and variational quantum algorithms for predictive modelling. We discuss the advantages and challenges associated with each approach and propose strategies to address them effectively. Additionally, we provide insights into the selection of appropriate evaluation metrics and validation techniques tailored to the unique characteristics of QML models.