The Arabic Factoid Question Answering System (QAS) serves as a critical tool in Natural Language Processing (NLP), designed to provide precise answers to user queries. Factoid QAS, which focus on concise, factual responses, are particularly significant as they address the most common user queries in applications such as education, healthcare, and digital assistants. However, Arabic QAS development has lagged behind other languages due to the complexity of the Arabic language and a lack of linguistic resources. This survey offers a detailed exploration of Arabic factoid QAS, presenting a synthesis of existing systems, highlighting their challenges, and proposing actionable insights for future advancements. Unlike previous reviews, this work identifies critical gaps in dataset availability, evaluates the impact of advanced Machine Learning (ML) and Deep Learning (DL) techniques, and emphasizes the role of factoid QAS in driving broader NLP applications. By bridging these gaps, the survey lays a foundation for developing efficient Arabic QAS that can meet the growing demand for intelligent systems in Arabic-speaking communities.