An anomaly, defined as something that deviates from what is normal, expected, or usual. It signifies abnormality or an irregularity that stands out from typical behaviours or patterns. Detecting anomalies is significant among numerous sectors due to the reasons of signal potential difficulties or opportunities. For an instance, in retail, detecting anomalies in sales data might prompt for further analysis into operational issues or customer behaviour to reduce losses and capitalize on its trends. Hence, different techniques are used for Anomaly Detection. However, anomaly detection using manual method are measured for time consuming, prone to error and can be tedious process. Therefore, different approaches have been considered for anomaly detection as AI (Artificial Intelligence) methods are efficient, faster, provides high level accuracy by effectively detecting the abnormalities. Owing to these aspects, this paper focuses on compiling different techniques and emphasizes on reviewing all anomaly detection using numerous techniques like ML (Machine Learning) and DL (Deep Learning) classifiers, statistical methods, one-class classification, clustering and density-based models which helps with identifying and comprehending the diversity of detection techniques that are applied in various domains like finance, retail, healthcare and cyber security. Various existing researches on anomaly detection are reviewed in the study. In addition to an overview, certain studies also deals with applications of detection models and future trends are reviewed in precise. Finally, the challenges are identified through the analysis of existing researchers and future recommendations are provided for overcoming the gaps that are intended to create promising work in this area.