The exponential growth of Artificial Intelligence (AI) has significantly increased the reliance on Data Centers (DCs), making them crucial for processing and storing vast amounts of data. However, this surge in AI deployment has highlighted an environmental concern of Carbon Dioxide (CO2) emissions generated by the DCs. These facilities are resource-intensive and demand substantial power to meet the computational needs of AI applications, thus contributing to a high carbon footprint. To address the issue, this thesis explores an innovative approach to measure the CO2 emissions by introducing a linear regression energy model based on Performance Monitoring Counters (PMCs) such as the total number of instructions and the total number of cycles of the computer processor and the development of energy-efficient AI models by optimising the hyperparameters and architecture of AI models to minimise the impact on the environment. The operational efficiency and environmental impact of DCs have been estimated based on metrics such as Power Usage Effectiveness (PUE), partial Power Usage Effectiveness (pPUE), and Carbon Usage Effectiveness (CUE). Several types of research have been conducted to optimise hardware such as processor idleness, power supply to the machine, cooling machines for the system, and selecting training locations with low carbon intensity to lower energy consumption. However, such improvements are insufficient since inadequately developed AI models can drastically drain the processor power. Therefore, engineers should focus on developing highly efficient and computationally feasible models. During this thesis, PMCs are used to estimate the computational complexity of AI models running on processors. It has been observed that processor-specific PMCs, like the total number of instructions and the total number of cycles collected during processing, strongly correlate with the processor’s energy consumption. They also impose very minimal overhead on energy utilisation, making them ideal for usage with AI applications. Therefore, PMCs have been used to calculate the energy consumption of processors and the DCs they are placed in. Central to our research is the formulation of an energy model that utilises PMCs to estimate processors’ energy consumption and CO2 emissions. By training various AI models on the Central Processing Unit (CPU), collecting Performance Monitoring Counter (PMC) data, and their associated energy consumption, a linear regression energy model to estimate the energy usage of AI applications is established. Subsequently, the CO2 emissions of applications running on these Central Processing Units (CPUs) are also calculated. For the simplicity of this research, only CPU and Dynamic Random Access Memory (DRAM) are taken into consideration, as they consume the maximum energy in comparison to other parts of the processor. This linear model produced an error of only 0.158% for CPU and 0.272% for DRAM. Further, the implications of hyperparameter optimisation and model architecture on energy consumption and CO2 emissions have been studied based on PMCs with a tradeoff in accuracy. This research will enable the estimation of energy consumption and CO2 emissions of AI applications based on inbuilt PMCs, and also reduce energy consumption and CO2 emissions by modifying the model architecture and hyperparameters while maintaining a tradeoff between accuracy and energy consumption.