Advances in language models have enabled significant strides in developing language technologies tailored for analyzing and processing Dialectical Arabic (DA), which exhibits unique linguistic features and variations compared to standard Arabic. This progress has sparked a surge of interest in various research tasks within the Arabic Natural Language Processing (ANLP) domain, encompassing areas such as sentiment analysis, dialect identification, normalization and classification, fake news detection, and part-of-speech tagging. The primary objective of this survey paper is to provide a comprehensive overview of the advancements made in dialectical ANLP from 2014 to 2024. A thorough analysis is undertaken, covering a corpus of approximately 200 research papers, to offer insights into the latest developments, resources, and applications concerning dialectical Arabic. By identifying and discussing the challenges and opportunities for future research, this study aspires to serve as a valuable reference for researchers, practitioners, and enthusiasts interested in the subject matter. Central to the investigation are the recent strides in natural language processing techniques that pertain to dialectical Arabic, namely DA sentiment analysis, DA identification, DA classification, DA normalization, DA part-of-speech tagging, and the role of DA in fake news detection, among other applications. Each research category is meticulously examined, providing a comprehensive understanding of their respective contributions, significance, encountered challenges, and the availability of pertinent datasets. This exhaustive survey paper encompasses existing studies within dialectical Arabic research categories. As a result, readers are presented with a detailed reference source in pursuing advancements and innovations within this field.