aspg-18-3517-1792025589 20261015005309 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 15 2 Zainab Rustum Mohsin College of Computer Science and Mathematics, University of Thi-Qar, Thi-Qar, Iraq Firoj Khan PGT(IT), Navodaya Leadership Institute, South Goa, India Software effort estimation is highly significant for project management regarding the bidding process since underestimation leads to financial losses, while overestimation may bring the chance of losing a competitive bid. Whereas numerous models have been designed up until now, those developed upon machine learning, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN) have emerged as preeminent technologies. The proposed research will explore the effectiveness of using the ANN and ANFIS approaches in the estimation of effort for NASA datasets by 13 observations used for training and the rest for the test. To check the precision of models, several measures are used to evaluate the accuracy of the developed model, including the correlation coefficient, RMSE, and MMRE. The findings demonstrate that ANN and ANFIS exhibit superior performance, yielding much higher prediction accuracy compared to conventional Models including Walston-Felix, Doty, Bailey-Basili, and Halstead. It emphasizes ANN and ANFIS as reliable and straightforward software effort estimating methodologies, hence yielding significant enhancements in estimation precision and competitiveness. Their high performance underlines their usefulness to project managers who seek accurate predictions. This study strongly recommends the application of data-driven approaches like ANN and ANFIS to enhance the overall estimation accuracy in software project bidding. 2025 29 40 3517 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.150203 https://americaspg.com/journal/18/article/3517 https://americaspg.com/storage/91737937861.pdf