Entity disambiguation refers to the resolution of uncertainties associated with entities found in the web of data, which are frequently used to represent information on the Semantic Web. The goal is to distinguish and set forth mentions to entities referenced on the semantic web, wherein such mentions may be ambiguous due to the synonymous representation of various concepts or entities. This paper presents a hybrid ensemble classifier-based approach in order to tackle the issue of author name disambiguation which is an entity disambiguation task on the Semantic Web. The study tested the proposed method on the OAEI 2015 dataset and showed it effectively distinguishes entities. Different machine learning methods were compared using metrics like accuracy, precision, recall, and F1-score. This research contributes to improving entity disambiguation in the Semantic Web. Future directions for research are discussed, emphasizing the need for ongoing innovation in this field.