Cervical cancer is a major global health issue. Early detection is key to reducing death rates and improving patient outcomes. Traditional screening methods, like cytological analysis and clinical risk assessment, often have limitations when used on their own. This can lead to uncertainty in diagnosing borderline cases. To tackle this problem, this study introduces PixelToPrognosis, a decision support system that combines image-based deep learning with clinical data analysis to improve cervical cancer risk prediction. The system uses EfficientNetB0 to extract important features from cervical cytology images. It then applies Particle Swarm Optimization (PSO) for effective feature selection. An SVM classifier predicts cell class and cancer risk from the image features. Meanwhile, a Random Forest model independently assesses clinical risk factors. Decision-level fusion occurs by averaging probability outputs from both models to create a strong final prediction. The system operates through a web-based interface that offers clear risk categories, color-coded cell class visualizations, and helpful clinical insights. Experimental results show high predictive accuracy, emphasizing the system’s potential to assist clinicians in early screening, risk stratification, and informed decision-making in cervical cancer diagnosis.