Accident detection plays a pivotal role in enhancing road safety and minimizing the impact of unforeseen events. This research addresses the challenge through the lens of deep learning, employing a comprehensive analysis of four state-of-the-art convolutional neural network models— ResNet50, VGG-19, DenseNet201 and Ensemble Model (EfficientMobileNet). In this work, we develop a robust multiclass accident detection system capable of accurately classifying diverse accident scenarios from visual data. The experiments reveal distinct performance characteristics, shedding light on the strengths and weaknesses of the individual architectures. In our work we collect almost 4800 images data which is collected from various sources like traffic monitoring systems, surveillance cameras, CCTV footage and videos. Dataset contains four classes (bike, bus, car, non-accident) and after training all pre-trained models among them DenseNet-201 outperforms others with the accuracy of 98.24%. This study contributes to the ongoing discourse on leveraging deep CNNs for safety-critical applications, offering valuable insights for researchers and practitioners alike.