Twitter-Bot is challenged to fight the attacks, that harm society. Getting label data for monitored bot detection is difficult and time-consuming. This study suggests an innovative unsupervised cluster method to effectively identify bots on Twitter using non-labelled data. The primary goal is a dependable system that can detect the bots properly, which can reduce misinformation and improve online chat. The proposed technology takes into account many important factors, including accuracy, computational complexity, and delayed handling. The system includes feature extraction and bot prediction steps, which use the adaptive DBSCAN algorithm. To validate the effect of technology, the study appoints various evaluation measurements, such as homogeneity (0.988), completeness (0.989), V-measure (0.989), adjusted Rand index (0.996), adjusted mutual information (0.989), silhouette coefficient (0.786), and Fowlkes Mallow's score (0.998). The results show the possibility of effectively labelling Twitter data and offer a practical and cost-effective approach to the success of technology and bot identity. This research emphasizes the importance of integrating several unsupervised algorithms to feature extraction, dimensionality reduction, and improved bot detection. This makes a significant contribution to the BOT detection and analysis of social media and creates broad opportunities to analyse Twitter data.