During the last decade, audio streams became an essential and fast means of communication through personal and business applications including social media and telehealth applications. Thus, various research efforts tried to develop robust and secure audio encryption algorithms that keep audio communications secure to the highest extent. Biological sequences retain huge amount of information which present new horizon over legacy encryption algorithms in terms of encoding capacity. This article introduces an intelligent audio encryption and compression framework, namely Audio-to-Peptide (A2P), that mimics the successive generation of biological sequences to successively encrypt and compress sequences of frames in raw WAV audio files. The parameters of the basic encryption key include some general information of the audio file in addition to some technical information that is based on the frequency of the audio to be encrypted. Hence, the proposed framework uses an Artificial Neural Network (ANN) model that was trained to accurately determine these technical parameters of the basic encryption key without any user involvement. The experimental results showed that the proposed algorithm is robust and secure against known security attacks.