Selective Laser Sintering (SLS) has emerged as a prominent additive manufacturing technology, enabling the creation of complex and intricate parts with various materials. Despite its numerous advantages, the SLS process can be sensitive to various factors that influence part quality, such as laser energy deposition and material behavior. In order to forecast and optimize process parameters, resulting to improved component quality, this work offers a thorough research on the construction of process models and simulations for SLS. The study employs a multi-scale and multi-physics approach, integrating thermal, mechanical, and fluid dynamics simulations, to capture the intricate interactions among the laser, powder bed, and material properties. The created models take into consideration important SLS process factors as laser power, scanning speed, layer thickness, material properties, and powder characteristics. The simulations enable accurate predictions of part quality attributes, including dimensional accuracy, porosity, and mechanical properties. In order to increase component quality and save production time, machine learning methods like neural networks and genetic algorithms are used to adjust process parameters based on simulation results. The study validates the models and optimization techniques through experimental data, demonstrating a high degree of accuracy and reliability. In conclusion, this research contributes significantly to the understanding and optimization of the SLS process, providing a robust framework for predicting and enhancing part quality. The proposed models and simulations can be employed in various industrial applications, facilitating the adoption of SLS technology and fostering innovation in additive manufacturing.