<![CDATA[Abstract. The advancement of artificial intelligence and Natural Language Processing (NLP) has greatly impacted Arabic language communication, facilitating progress in areas such as machine translation, sentiment analysis, speech recognition, and dialect identification. Despite these technological advancements, Arabic continues to pose significant challenges due to its diglossic nature, complex morphology, and extensive dialectal diversity. This study reviews the current state of AI-driven Arabic language technologies, assessing their effectiveness and identifying key gaps, including the scarcity of annotated datasets and insufficient adaptation to dialectal variations. Through qualitative content analysis of recent research and practical applications—ranging from dialectal corpora like MADAR to Arabic-specific models such as AraBERT—this paper highlights the increasing capabilities of AI in addressing Arabic communication tasks. The findings emphasize the need for region-specific linguistic resources, culturally informed AI design, and the ongoing development of dialect-aware NLP systems. Future directions call for a collaborative, interdisciplinary effort to create inclusive, accurate, and adaptable tools for Arabic-speaking communities worldwide.]]>
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