Fuzzy logic testing is a kind of soft computing that employs ambiguous and graded truths to figure out how excellent software is and how to run tests. This method comes from the field of fuzzy logic. This paper provides a comprehensive and thesis-oriented analysis of fuzzy logic in the context of software testing. The study provides a comprehensive literature review, well defined research objectives, and a methodology for developing fuzzy inference systems (FIS) intended for test-case prioritisation, dataset creation, and experimental validation. We develop a fuzzy-priority test-case scheduler and compare it against baseline approaches using a realistic synthetic dataset with 500 test modules. This is done to prove how useful the scheduler is in real life. The results, shown through fault detection curves, priority-score distributions, and a publicly available experimental dataset, show that using fuzzy rules to prioritise considerably improves the ability to find faults early compared to using simple heuristics. This is especially true when you don't know how often problems happened in the past, how much it will cost to run, or how hard the code is to comprehend. The results suggest that fuzzy logic is a simple and useful approach to test for regression.