Modern software quality engineering requires testing approaches capable of addressing increasing application complexity, rapid development cycles, heterogeneous interfaces, and evolving security requirements. AI-driven test case generation and optimization offers a pathway from manually constructed and largely static test suites toward adaptive mechanisms capable of identifying relevant test conditions, prioritizing high-value scenarios, and reducing redundant execution. This research and review paper examines the conceptual foundations of AI-driven test case generation by integrating the provided literature on graphical authentication, usability, security, and automated software quality engineering. Particular attention is given to how structured representations of user interaction, authentication behavior, security threats, and usability constraints can inform intelligent test design. The paper develops a conceptual AI-driven test case generation and optimization framework consisting of requirement interpretation, behavioral modeling, test generation, risk assessment, prioritization, execution feedback, and continuous optimization. The review identifies that graphical authentication research demonstrates the importance of covering diverse interaction patterns, user choices, usability conditions, and attack scenarios, while contemporary AI-oriented software quality engineering provides the broader automation context (Ramamurthy, 2023). The proposed framework emphasizes coverage, fault-detection potential, risk, historical execution information, and redundancy as optimization dimensions. The findings indicate that AI-assisted testing can improve the strategic allocation of testing effort when generation and prioritization are treated as interconnected activities rather than independent processes. However, limitations remain concerning training-data quality, explainability, false prioritization, domain transfer, and the dependence of intelligent systems on reliable behavioral representations. The paper concludes that AI-driven test engineering should augment, rather than completely replace, systematic testing principles and should incorporate continuous feedback, measurable coverage, and human validation.