An Intelligent Airbag Deployment System has been developed to address the suboptimal performance of current airbag systems, particularly in budget-friendly vehicles, which often suffer from unexpected deployments or failures to deploy during critical incidents. These issues typically arise from a reliance on predefined crash algorithms and sensor data that may not adequately capture complex real-world collision dynamics, thereby compromising passenger safety. This research introduces a novel approach that leverages machine learning and computer vision to overcome these limitations. The system integrates the YOLOv8 object detection model with a Logistic Regression classifier to anticipate accidents in real-time by analyzing live dashcam footage. Key visual data features are extracted, preprocessed, and used to train the model to predict the necessity of airbag deployment. The model's performance was fine-tuned through hyperparameter optimization and assessed using standard metrics like accuracy, precision, recall, and F1-score. Testing in both simulated and real-world environments demonstrated significant improvements in deployment precision and a reduction in false activations compared to conventional systems. Initial evaluations showed the system's capability to differentiate between crash and non-crash events, achieving an accuracy of 60.8%, a precision of 57.1%, and an F1 score of 26.1% in predicting correct airbag deployment events. This system aims to enhance passenger safety and mitigate economic burdens associated with unnecessary deployments