The macro placement step in physical design remains one of the few stages still executed manually by engineers, despite being time-consuming and repetitive. This thesis investigates the automation of this process through the use of a machine learning-based macro placer tool developed in-house at Qualcomm Incorporated. The primary objective of this work is to evaluate the quality and impact of tool-generated macro placements in comparison to traditional manual methods. The thesis is structured to provide both theoretical and practical context for this work. The first chapter introduces the technology scaling trend in the VLSI industry and highlights the growing need for Electronic Design Automation (EDA) solutions, such as the macro placer tool investigated in this work.