aspg-28-4125-1792155167 20261016125247 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 10 2 Ahmed Hamid Elias College of Health and Medical Techniques, Al-Furat Al-Awsat Technical University, Najaf, Iraq Dhurgham Abbas Mohsin Albojwaid Jabir Ibn Hayyan University for Medical and Pharmaceutical Sciences, Central Library Automated Systems Department, Najaf, Iraq Ahmed Younus Abdulkadhim College of Health and Medical Techniques, Al-Furat Al-Awsat Technical University, Najaf, Iraq Raad S. Alhumaima Brunel University, Uxbridge UB8 3PH, United Kingdom Laith Farhan School of Engineering, Manchester Metropolitan University, Manchester, M1, United Kingdom Integration of quantum-inspired algorithms in machine learning has opened up new horizons for improving predictive performance, efficiency, and scalability across a broad spectrum of application domains. This paper presents a comparative investigation between traditional machine learning techniques and quantum-inspired models. Experi-ments demonstrate that quantum-inspired approaches exhibit higher accuracy, training effectiveness, and stability on difficult datasets than traditional methods. Results point towards higher convergence rates, shorter runtime, and enhanced generalization capacity in quantum-inspired models, realized in the form of enhanced accuracy, precision, recall, and F1-scores. Receiver operating characteristic (ROC) and precision–recall analyses further confirm the superior discriminative power of quantum-inspired approaches. Results point toward the potential of quantum-inspired machine learning as an interface between conventional algorithms and the new frontier of quantum computing, with a stepping stone to future-proof intelligent systems. 2025 36 45 4125 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.100205 https://www.americaspg.com/journal/28/article/4125 https://www.americaspg.com/storage/articles/manuscripts/01M4J5HWK0JXGT5FX82J61F4TD.pdf