The rapid evolution of machine learning (ML) has transformed industries by enabling automation, prediction, and optimization for complex real-world problems. However, developing ML pipelines involves repetitive tasks such as data preparation, model building, and evaluation, which are time-consuming and prone to errors. This paper introduces an automated system for generating ML code using Jinja2 templating and supervised MLbased feature prediction. The system analyzes 5000 ML code templates to extract parameters like data type, preprocessing techniques, model architecture, and hyperparameters. A supervised ML model predicts missing parameters based on partial user input, enabling dynamic code generation. The framework supports diverse data formats (tabular, image, text) and ML tasks (classification, regression). Experimental results demonstrate high accuracy in parameter prediction and significant time savings (70–80% reduction in setup time). The system simplifies ML development, reduces errors, and accelerates experimentation, making it accessible to researchers, developers, and students.