aspg-21-3709-1791465335 20261008131535 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2025 26 2 Kesavulu Poola Associate professor, Center for Management Studies, Jain University, Bengaluru, India V. Pavankumari Assistant professor, G. Narayanamma Institute of technology and science, Hyderabad, India J. Anil Kumar Associate professor, Sri Venkateswara College of Engineering (Autonomous), Tirupati, India Akkyam Vani Research Scholar, Department of Statistics, Sri Venkateswara University, Tirupati, India Asif Alisha S. Associate Professor, Department of mathematics, School of Liberal Arts and Sciences, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India A. Srinivasulu Guest faculty, Department of Statistics, Vikrama Simhapuri University, Nellore. India Regression modeling is a significant statistical tool aimed at quantifying and understanding the nature of relations between the predictor and response variables. The routine parameter estimation procedures, like OLS and ML, are based heavily on the assumption of normality in data, which will not be the case for most real-world data scenarios. The paper presents a Neutrosophic approach for the estimation of parameters in multiple linear regression models, making use of the Neutrosophic principles to treat uncertainties, indeterminacies, and inconsistencies in actual data, a proposed method is called the Simple Averaging Method, or SAM. This is a robust alternative to traditional methods and provides reliable results even if the assumptions of normality are not held. SAM performance is tested using real-time crime data in the USA and demonstrates its capabilities to deal with complex datasets. The comparative analysis between the OLS model and the same model is done via RMSE and MAD metrics. The results show that SAM significantly outperforms OLS with an RMSE of 34.37598 in contrast to 58.05248 for OLS. Graphical analysis further confirms SAM's performance over and above OLS. Critical issues of regression modeling with incorporation of neutrosophic logic cover their critical challenges, especially when standard assumptions are violated. 2025 215 228 3709 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.260216 https://www.americaspg.com/journal/21/article/3709 https://www.americaspg.com/storage/41741995611.pdf