The detection of gas leakage is a very important aspect of safety planning because gas leakage is a critical problem that can happen in both industrial and home settings and can be fatal in many cases. Therefore, if such coves are going to be used, the biggest concern would be to ensure that whatever system is being thought about will be accurate and reliable, without catastrophically thousands die. All the popular research work done for the energy conservation in this paper till date uses the machine learning techniques for the gas leakage detection but this paper focused on analyzing each technique using different algorithms and numbers of neurons to test how they fit for the energy conservation. These algorithms are “Random Forest,” KNN, “Decision Tree,” SVM, and “Logistic Regression.” Each of developments that were three composed through the cross-validation and hyperparameter fine-tuning in request to increment the exactness and strength of the models that were included. KNN was done better among the models with 0.9969 CV score, 0.9875 accuracy, 0.9997 ROC-AUC score, whereas KNN is followed by Random Forest with 0.9844 CV score and 0.9998 ROC-AUC score. Results show that KNN and Random Forest can earn good accuracy and are useful in building efficient and scalable gas leakage detection systems. The systems can also be integrated with active sensor networks deployed in the factory premises for real-time monitoring and this serves as a potential improvement in safety conditions.