Manjeet Kour,Er. Shilpa 02 07 Background The increasing complexity of software systems and accelerated development cycles have intensified the need for intelligent test management systems that optimize decisionmaking during software testing processes Objective This narrative review examines the current state of intelligent test management systems evaluating their effectiveness in optimizing testing decisions identifying key technologies and assessing implementation challenges Methods We conducted a comprehensive review of 25 verified studies published between 20072024 sourced from IEEE Xplore ACM Digital Library ScienceDirect and other reputable databases Studies were selected based on their focus on AIML applications in software testing and empirical validation of intelligent decisionmaking systems Results Our analysis reveals that AIpowered test management systems achieve 8595 accuracy in automated testing decisions with 4060 reduction in test execution time and 20 50 cost savings Machine learning classification techniques dominate current implementations followed by genetic algorithms and natural language processing The ITMOS framework demonstrates industrial viability with F1scores of 09163 for CI configuration and 09463 for test automation decisions Conclusions Intelligent test management systems show significant promise for transforming software testing practices through automated decision support predictive analytics and adaptive optimization However challenges remain in integration complexity tool reliability and organizational adoption Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026