An article by programmer Nour Hassan Obeid titled "Model Compression and Algorithm Efficiency Improvement" Model compression techniques are a cornerstone of modern artificial intelligence development. This field aims to reduce the size of large neural networks to enable them to operate with high efficiency. The importance of these processes lies in facilitating the running of models on devices with limited resources. The trimming technique removes non-essential weights within the neural network structure. This step reduces the number of parameters without compromising the predictive accuracy of the model. In addition, quantization emerges as a powerful tool for reducing the computational accuracy of data. Weights are converted from floating-point to integers to reduce memory usage. This procedure significantly accelerates reasoning processes within processors and smart chips. Knowledge distillation also stands out as an innovative solution for training small models. The student model mimics the behavior of the large teacher to acquire programming capabilities. This method is an effective way to transfer experience between different generations of algorithms. Furthermore, algorithm efficiency is improved through re-engineering computational processes. Discrete convolutions are used to reduce the computational effort at each layer. These improvements contribute to reduced response times in real-time applications. They also significantly reduce power consumption in mobile and smart devices. Reducing the carbon footprint of data centers is a key environmental objective for these technologies. These processes require a thorough understanding of linear algebra and the architecture of modern computing systems. Balancing accuracy and speed is the greatest challenge for software engineers worldwide. These technologies enable the integration of artificial intelligence into Internet of Things (IoT) applications. This integration contributes to the development of smart technological solutions serving the medical and autonomous driving sectors. Continued research in this field ensures that the technology remains advanced and sustainable. These methodologies enhance the quality of the user experience by reducing delays in command execution. These innovations are essential to keep pace with the information explosion in the era of big data. The ability to compress models means democratizing access to advanced artificial intelligence. Global laboratories continue to search for new ways to make algorithms more agile. This article summarizes the scientific path toward building intelligent systems that combine power and speed. The future of technology lies in creating intelligent software solutions that adapt to limited resources. Thus, we achieve green, efficient, and scalable artificial intelligence across various fields. Our commitment to scientific development opens new horizons for technological innovation in our beloved country. Al-Mustaqbal University, ranked first among Iraqi private universities.