Abstract Travel time plays a crucial role in the intelligent transport system in metropolitan cities. Predicting accurate Taxi trip travel time helps commuters to plan their trip better and reach the destination on time. Most of the existing techniques use supervised learning models to estimate the travel time. Performance obtained from the supervised learning models is insufficient. In this paper, we propose a novel approach that aims at predicting travel time by using both supervised and unsupervised techniques with a large historic dataset, and this novel method is compared with supervised techniques. The clustering approach of unsupervised learning along with supervised helps to enhance the performance of a predictive model. Clustering helps in segmenting the nearby location data into a similar group which helps in finding the underlying pattern within the large dataset. Then, a supervised algorithm is applied to this clustered data. Machine Learning (ML) techniques such as Random Forest Regressor (RFR), XGBoost Regressor (XGBR), which are supervised and RFR with k-means, XGBR with k-means which combines both supervised and unsuper- vised techniques are used to predict the trip time of the taxi trips. The results show that a combination of supervised and unsupervised models perform better than only supervised models. Also, the comparison shows that the RFR and RFR with k-means perform better than XGBR and XGBR with k-means respectively. RFR with k-means outperforms other models with an accuracy of 84.6%. With better performance, RFR with k-means also reduces the error rate of the model significantly. Keywords Intelligent Transport System · Machine learning techniques in ITS · Route guidance system · Travel time prediction · Traveller Information System