Anomaly detection plays a pivotal role across various domains. This paper introduces a comparative analysis between two prevalent anomaly detection techniques: Histogram-based Outlier Detection (HBOS) and Isolation Forest. A comprehensive elucidation of the operational mechanisms of each model is presented along with empirical investigations conducted on authentic datasets. The efficacy of both approaches was assessed through performance metrics, including accuracy. Our findings demonstrate that both HBOS and Isolation Forest attained a commendable accuracy rate of 95% in detecting normal instances, besides encountering challenges in flagging anomalies. Notwithstanding their limitations in anomaly detection, these models furnish valuable insights for academics and professionals in the anomaly detection domain, facilitating sound decision making in selecting the optimal model for their requirements.