Accurate thermal imaging of pipelines is critical for monitoring and diagnosing various industrial processes, including leak detection, structural integrity assessment, and thermal performance evaluation. A key challenge in obtaining reliable temperature readings from thermal cameras is correctly setting parameters such as emissivity and reflected temperature. While most environmental factors, such as ambient temperature and humidity, are easily measurable, emissivity and reflected temperature require more complex determination methods due to the unique thermal properties of pipeline insulation materials. This paper presents a method for determining emissivity and a linear regression model for estimating reflected temperature in aluminum-insulated pipelines. By combining experimental measurements with statistical modeling, the proposed approach enhances the precision of temperature readings in thermal imaging systems. The methodology supports applications ranging from early leak detection and preventive maintenance to energy efficiency monitoring. The model is validated through case study demonstrating its adaptability to real-world scenarios, reducing errors and improving decision-making accuracy. This research contributes to advancing thermal diagnostic techniques by providing a practical framework for accurately configuring thermal cameras in industrial pipeline systems.