Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4125 2022 2022 College of Health and Medical Techniques, Al-Furat Al-Awsat Technical University, Najaf, Iraq Ahmed Ahmed Jabir Ibn Hayyan University for Medical and Pharmaceutical Sciences, Central Library Automated Systems Department, Najaf, Iraq Dhurgham Abbas Mohsin Albojwaid College of Health and Medical Techniques, Al-Furat Al-Awsat Technical University, Najaf, Iraq Ahmed younus abdulkadhim Brunel University, Uxbridge UB8 3PH, U. K Raad S. Alhumaima School of Engineering, Manchester Metropolitan University, Manchester, M1, UK Laith Farhan 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. Experimental experiments 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 2025 67 81 10.54216/JAIM.100205 https://www.americaspg.com/articleinfo/28/show/4125