The visual sensory organ (VSO) serves as the primary channel for transmitting external information to the brain
therefore, damage to the VSO can severely limit daily activities. Visual-to-Auditory Sensory Substitution (V2A-SS), an innovative approach to restoring vision, offers a promising solution by leveraging neuroplasticity to convey visual information via auditory channels. Advances in information technology and artificial intelligence mitigate technical challenges such as low resolution and limited bandwidth, thereby enabling broader applicability of V2A-SS. Despite these advances, integrating V2A-SS effectively into everyday life necessitates extensive training and adaptation. Therefore, alongside addressing technical challenges, investigating effective learning strategies to accelerate the acceptance of V2A-SS is crucial. This study introduces a V2A-SS learning model based on the Information Processing Learning Theory (IPLT), encompassing the stages of "concept acquisition, rehearsal, assessment" to reduce the learning curve and enhance adaptation. The experimental results show that the proposed learning model improves recognition rates, achieving an 11% increase over simple random repetition learning. This improvement is significantly higher than the gain of 2.72% achieved by optimizing the V2A-SS algorithm with Mel-Scaled Frequency Mapping. This study suggests that a structured learning model for sensory substitution technologies can contribute to bridging gaps between technical feasibility and practical application. This underscores the need to develop effective learning models, alongside technological advancements, to accelerate the adoption of V2A-SS and neuroplasticity.