The primary aim of this research is to develop and implement a piano evaluation mechanism enhanced with an error correction feature utilizing AI. Precisely, the study discovers to overcome the limitations of traditional piano teaching evaluations which frequently suffer from poor convergence and a tendency to fall into local extremes. We propose a novel AI method called Selfish Herds Search-integrated Improved Probabilistic Neural Network (SHSIPNN), for piano music evaluation. The method leverages the Improved Probabilistic Neural Network (IPNN) for its robustness in handling data and for improving convergence in learning. In addition to using the short-term energy difference (STED) technique to precisely determine the temporal assessment of every note in the audio of a piano performance. Additionally, the Discrete Wavelet Transform (DWT) is applied to assess pitch accuracy. The entire system is integrated within a Musical Instrument Digital Interface (MIDI) framework to facilitate detailed evaluation of piano performances. The piano performances are categorized as “Good,” “Fair,” or “Poor” in this examination, which is structured for a classification problem. Our findings emphasize the efficacy of the SHS-IPNN technique, as demonstrated by its overall performance in terms of recall (90.5%), accuracy (96%), F1-score (93.5%), and precision (95.5%). The experimental outcomes indicate that the SHS-IPNN model outperforms existing methods in terms of accurately detecting performance errors and evaluating piano performances. The model’s increased accuracy in providing expressive, rhythmic, and overall judgments is demonstrative of this development. The innovative application of the SHS-IPNN method in piano music education demonstrates a significant advancement in the field. This approach not only improves accuracy in performance evaluation but also improves the learning procedure by providing error correction, which is crucial for developing proficient piano skills.