Website phishing poses a massive security threat that continues to increase in prevalence. Internet scammers exploit human faith by running imitation websites which aim to obtain confidential user information. An extensive review of multiple phishing detection techniques and hybrid detection models appears in this paper which brings together different detection methods to speed up and increase the accuracy of breaking down phishing-related websites. The paper investigates how machine learning (ML), artificial intelligence (AI) and heuristic-based approaches and anomaly detection should be implemented within hybrid systems which detect phishing behavior. Multiple studies from the literature receive analysis through which we identify their research approaches as well as their outcomes together with their limitations along with their contributions to the field. The evaluation will demonstrate how hybrid models can boost the detection of phished emails while detailing methods to strengthen model performance and flexible design and growing capability.