Ensuring road safety is a critical concern globally, and understanding driver behavior plays a significant role in mitigating traffic accidents. This paper presents a novel approach to intelligent driver behavior analysis by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNN) and TensorFlow. Our methodology analyzes huge amounts of data to identify patterns that imply changes in driving behaviors. Our goal is to achieve high accuracy in classifying and predicting various driver actions by training a CNN model on this data. The proposed system is designed to process data, providing immediate feedback to drivers, and potentially alerting them to hazardous behaviors before accidents occur. The experimental results demonstrate that our model achieves superior performance compared to traditional methods, highlighting the efficacy of deep learning in enhancing road safety.