<p>With the emergence of disruptive technologies like VANETs, the Smart Transportation Framework plays a crucial role in day-to-day life demanding vehicular traffic prediction for a country like India. Although many researchers have recently studied this topic of predicting vehicle traffic flow, the anticipated estimation accuracy has not been achieved through traditional algorithms, thus demanding for more effective machine-intelligent algorithms. Further, there are very few research papers available in this direction, especially for Indian roads. This paper attempts to bridge some of these leftover gaps in the reported works. This paper proposes a novel algorithm namely “Multivariate N bEATS Algorithm” for predicting road vehicular traffic flow using the local vehicular observed traffic data of Puducherry City, India where all the authors reside. In this study, the traffic prediction algorithm performs forecasting traffic flow volume and congestion levels in specific areas of Puducherry city using historical traffic data combined with other pertinent factors such as weather, and events, and road conditions. The proposed multivariate N bEATS Algorithm outperforms existing algorithms already suggested for a similar study in terms of accuracy and precision. Performance Comparison analysis of the proposed algorithm in terms of Accuracy, Mean Absolute Error(MAE), Mean Squared Error(MSE) and Mean Absolute Percentage Error(MAPE) is found to be very appealing. Henceforth, this algorithm is very apt for traffic prediction and management systems for some Indian Cities similar to the one considered for the study.</p>