Gas sensors are widely used to detect the presence of hazardous gases in our daily lives, and their accuracy is crucial for ensuring the safety of individuals and environments. Gas sensors are essential in a variety of applications, such as environmental monitoring, industrial safety, and healthcare. These sensors are intended to detect and measure the presence of certain gases in their surroundings. Significant progress has been achieved in the development of gas sensor technology in recent years, resulting in better sensitivity, selectivity, and miniaturization. In this paper, we propose an optimized deep-learning approach for gas sensor data analysis that improves gas prediction accuracy. The proposed approach includes advanced data preprocessing techniques, feature selection, and model optimization to increase gas prediction performance. The contribution of this research is the development of a novel deep learning-based approach that optimizes the accuracy of gas prediction, making it more trustworthy and practical for real-world applications. The proposed method has significant implications for gas detection and can potentially save lives by providing early warning of dangerous gas levels.