In addition to surgical treatment, postoperative targeted speech therapy is an important part of the comprehensive team approach of cleft lip and palate(CLP). As the most widely used assessment for speech recognition, the main limitation of subjective assessments is its personal tendencies on results. Therefore, the precise system remains to be further improved. With the promotion of multidisciplinary crossing and integration, computer science has become the cornerstone of a high-quality health care system, and artificial intelligence (AI) has been applied to the field of speech disorders preclinically. Various studies focused on computer-aided recognition and evaluation technology of cleft palate speech, in which compensatory pronunciation errors, such as consonant ellipsis and pharyngeal fricative, were the key features for recognition and analysis. Although the combination with software and hardware improved the accuracy of recognition, current studies were primarily limited to independent acoustic feature identification, and a multiparameter analysis is needed to avoid systematic deviation. It lacks the attempt of treatment with computer science in speech evaluation of cleft palate, and the online platform has not been fully used. The use of AI and subjective evaluation systems is one of the prospect models of the comprehensive team approach of CLP.