This study examines Arabic Information Retrieval (IR) methods, comparing traditional techniques such as Boolean retrieval, Vector Space Model (VSM), and Term Frequency-Inverse Document Frequency (TF-IDF) with hybrid approaches that integrate machine learning and word embeddings, particularly Continuous Bag of Words (CBOW). Using standard Arabic corpora and benchmark datasets, we evaluate retrieval performance based on precision, recall, mean average precision (MAP), F1-score, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). While IR has significantly advanced in English-language systems, Arabic IR remains relatively underexplored, necessitating further development and evaluation. Our findings highlight the challenges posed by Arabic morphology and linguistic diversity, while also identifying opportunities to improve retrieval accuracy. This study underscores the need for deep learning integration, cross-linguistic adaptation, and the development of specialized benchmark datasets to advance Arabic IR research.