Monitoring emissions is a critical challenge encountered by numerous industries globally, with the Gas Turbine Industry being particularly affected. The significance of monitoring emissions lies in its ability to assess the performance of these systems and identify their environmental impact. In this paper, we propose multiple machine learning and deep learning models for regression analysis. Our primary objective is to predict Carbon Monoxide and Nitrogen Monoxide emissions from Gas Turbines accurately and compare each model based on their accuracies. By employing machine and deep learning techniques, we aim to develop a robust model that can aid in better understanding emissions patterns and contribute to devising effective strategies for emission reduction. The Random Forest model displayed the lowest mean absolute error at 0.487 and 2.568 for CO and NOx, respectively.