Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3266 2019 2019 Research Scholar, Department of ECE, Chaitanya Deemed to be University, Hanamkonda, Warangal, Telangana, India Appanaboyina Appanaboyina Professor, Department of ECE, Chaitanya Deemed to be University, Hanamkonda, Warangal, Telangana, India Seetharam Khetavath Fatty liver disease, encompassing conditions like NAFLD (Non-Alcoholic Fatty Liver Disease) and NASH (Non-Alcoholic Steatohepatitis), is a significant global health issue linked to metabolic syndrome and increasing incidences of liver-related complications. Accurate and early detection of fatty liver illness is critical for effective intervention and management. This paper proposes a novel method for the prediction and arrangement of fatty liver disease using Probabilistic Neural Networks (PNNs), leveraging advanced machine learning techniques to enhance diagnostic accuracy and reliability. We developed a PNN-based model to classify liver conditions from a dataset comprising clinical and imaging features, including liver fat content, texture metrics, and demographic information. The PNN was chosen for its capability to handle complex, high-dimensional data and provide probabilistic outputs, which are crucial for assessing the likelihood of different disease stages and improving interpretability. The proposed methodology includes preprocessing steps to normalize and augment the data, followed by feature extraction using advanced techniques to capture relevant patterns. The PNN architecture was designed with multiple layers to process features and deliver class probabilities. The method's concert was estimated utilizing average system of measurement such as accuracy, precision, recall, and F1-score, demonstrating its efficacy in distinguishing between different stages of fatty liver disease. Experimental results indicate that the PNN model achieves high classification accuracy and outperforms traditional machine learning methods in detecting fatty liver illness. This study highlights the potential of PNNs in enhancing diagnostic processes and providing a robust tool for clinicians. Future work will concentrate on expanding the dataset, refining the model, and integrating it into clinical workflows to support better patient outcomes in liver disease management 78 90 10.54216/JISIoT.140207 https://www.americaspg.com/articleinfo/18/show/3266