International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3709 2020 2020 Associate professor, Center for Management Studies, Jain University, Bengaluru, India admin admin Assistant professor, G. Narayanamma Institute of technology and science, Hyderabad, India V. Pavankumari Associate professor, Sri Venkateswara College of Engineering (Autonomous), Tirupati, India J. Anil Kumar Research Scholar, Department of Statistics, Sri Venkateswara University, Tirupati, India Akkyam Vani Associate Professor, Department of mathematics, School of Liberal Arts and Sciences, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India Asif Alisha. S. Guest faculty, Department of Statistics, Vikrama Simhapuri University, Nellore. India A. Srinivasulu 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 2025 215 228 10.54216/IJNS.260216 https://www.americaspg.com/articleinfo/21/show/3709