Acoustic feedback is a persistent challenge in audio closed-loop sys-tems such as hearing aids and public address systems, occurring when the sound emitted from the loudspeaker is recaptured by a microphone. The acoustic feed-back phenomenon limits stable gain and causes howling under some circum-stances. A common and effective solution is adaptive feedback cancellation (AFC), which uses adaptive filters to cancel the feedback signal, enhancing sta-bility. A key difficulty for AFC is the inherent correlation between the loud-speaker signal and the desired input (especially when the input signals are spec-trally colored, like speech, music, etc.), which leads to biased estimates of the feedback path. To counter this, methodologies such as the prediction error method (PEM) and its variant decorrelate the input signals of the adaptive filter, improving estimation robustness. The PEM integrated with a range of adaptive algorithms, including NLMS, IPNLMS, H-NLMS, APA, and IPAPA, has been proposed in the literature, each balancing convergence speed, computation, and steady-state error. Since implementing and comparing these algorithms in a unified, real-time context is still challenging, we introduce a comprehensive MATLAB/Simulink platform that provides an end-to-end AFC system. This plat-form enables users to seamlessly switch between various AFC algorithms, toggle pre-filtering, and monitor key performance metrics, such as normalized misalignment (MIS) and added stable gain (ASG) in real-time. This interactive environment serves as both a powerful educational tool and an efficient testbed for vali-dating novel AFC techniques.