Schizophrenia is a complex and serious mental disorder in millions around the world. Timely and correct detection is very crucial in effective treatment and therapy. This study investigates the efficiency of two popular ML algorithms, namely Random Forest and Logistic Regression, in detecting schizophrenia based on clinical data. In this respect, the Random Forest, a nonlinear supervised ML algorithm, is pitted against Logistic Regression, a linear supervised algorithm. Further, we analyze performance through different performance measures such as accuracy, precision, recall, and F1-score to provide comprehensive insight into predictive abilities. These results show that Random Forest can perform better in capturing nonlinear patterns in data, whereas Logistic Regression has advantages from interpretability and computational perspectives. This paper underlines the issue of model selection in schizophrenia early detection and draws the line for further research in this arena.