<p>Nowadays, classifying the behavior of the drivers is considered a real-time requirement in diverse contexts. Distraction during driving occurs when the drivers engage in various non-driving tasks that cause serious dilemmas in road traffic safety as well as road accidents. These non-driving tasks while driving significantly reduce the attention hence, the number of road accidents has increased and transportation is smashed. Therefore, an effective driver behavior detection system has been exploited widely to minimize the risk of road traffic accidents. A novel lightweight convolutional bidirectional long short-term memory-based transient search optimization algorithm is developed to detect the behavior of drivers and classify them based on the severity levels or class labels namely steady-state driving (class I), assertive driving (class II), absent-minded driving (class III), exhausted driving (class IV), Slow (class V), Sudden Acceleration (class VI), Sudden Right Turn (class VII), Sudden Left Turn (class VIII), Sudden Break (class IX) as well as alcoholic driving (class X). The proposed model systematically detects and generates the outcome effectively by focusing on the key aspects of the driver’s body movements. Various types of data namely gravity, speed, acceleration, vehicle throttle as well and Revolutions per minute are collected and provided as input. The proposed technique demonstrates high classification accuracy in detecting diverse driver behaviors under real-world conditionsand the accuracy rate attained by the proposed approach is 98.25%.</p>