Arabic is one of the oldest and richest languages in the world. However, the application of Artificial Intelligence (AI) in digitizing this language faces unique challenges, especially in syntax and morphology. This study discusses the problems AI applications face in processing and analyzing Arabic syntax and morphology, focusing on the technical and linguistic challenges that hinder the complete success of these applications. The researcher uses a descriptive-analytical approach, both qualitative and quantitative, which is done by collecting information/samples, selecting respondents, and analyzing information/samples—the complexity of morphology and syntax. Results: Arabic is characterized by a complex morphological and syntactic system, where many rules are not easily aligned with simple computational models. This includes verb conjugation, various sentence structures, and context-dependent pronouns. Diversity of forms and structures: Arabic words have many morphological forms used in different contexts, leading to differences in meaning. This variability makes machine translation and text analysis very challenging. Challenges in processing Arabic sentences: Arabic sentences differ from sentences in other languages regarding word order and pronouns, making it difficult for AI applications to interpret and extract meaning accurately. Limited resources: although there are some tools and libraries for Arabic, they are still limited compared to other languages, such as English, which reduces the effectiveness of models that can be used in applications. Processing methods; use of advanced machine learning techniques: By relying on machine learning techniques such as deep neural networks and big data analysis, models capable of understanding more complex linguistic structures can be developed. In developing large linguistic databases, it is essential to create databases that include a rich syntactic and morphological framework covering the vast diversity of Arabic, helping in model training and improving their accuracy. Research on hybrid approaches: combining traditional AI techniques with rule-based syntactic and morphological methods can help enhance sentence translation and analysis. Improved contextual processing: by developing models that handle texts in a broader context, errors can be reduced due to narrow interpretations or multiple meanings. Therefore, enhancing AI applications in the digitization of Arabic requires special attention to address the linguistic and technical challenges associated with syntax and morphology. Effective solutions can be developed through collaboration between linguists and AI engineers to make these applications more accurate and suitable for various uses in fields such as education, translation, and research.