aspg-3-3715-1791943538 20261014020538 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 2 Sudhirvarma Sagiraju Research Scholar, School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Jnyana Ranjan Mohanty Professor, School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Anima Naik Professor, Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, India Accurate disease prediction is essential for enabling preventive healthcare and reducing the burden of chronic illnesses. This study introduces an innovative multi-disease prediction framework leveraging machine learning and optimization techniques to address limitations in precision and scope present in prior research. Specifically, we focus on predicting five major diseases—diabetes, heart disease, kidney disease, liver disease, and breast cancer—by employing the Social Group Optimization (SGO) algorithm to fine-tune the Random Forest (RF) classifier's hyperparameters.The proposed SGO-optimized RF model optimizes seven critical parameters - n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, bootstrap, and criterion simultaneously, significantly enhancing the model's performance. Our approach, applied to five disease datasets, achieves notable accuracy improvements: 98.25 When tested on diverse datasets, the model achieves exceptional accuracy: 98.25% for breast cancer, 84.62% for liver disease, 93.44% for heart disease, 82.47% for diabetes, and 100% for chronic kidney disease. On average, the SGO-optimized RF outperforms existing methods with a 2.3% accuracy improvement across datasets. This research highlights the transformative potential of SGO-based optimization in advancing the accuracy and reliability of predictive models. The results demonstrate the framework's applicability in clinical scenarios, providing precise and actionable insights that support early diagnosis and improve patient outcomes. 2025 341 366 3715 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190225 https://americaspg.com/journal/3/article/3715 https://americaspg.com/storage/91742558314.pdf