With its ability to construct components through the layer-by-layer deposition of material, Additive Manufacturing (AM), more commonly known as "3D printing", has revolutionized the manufacturing industries. Not only can AM produce complex lightweight designs, but it can also streamline the supply chain, allowing businesses to more quickly and easily meet customer demand. Additionally, with the rising demand for low-volume customized products and sustainable production, manufacturers are increasingly compelled to adopt AM to remain competitive in the global economy. Despite its popularity, AM has several significant drawbacks, one of the most notable being its poor surface topography quality. Most product failures can be traced back to the initial surface conditions, making the surface texture a crucial factor in determining how well a product will perform. Hence, this thesis presents a study on the surface topography of various AM processes, mainly to understand the surface behavior in relation to the factors affecting it. Every manufacturing process, including AM, generates distinct surface features referred to as “footprints” or process signatures, which substantially affect the surface quality and function. These process signatures vary based on changes in AM processes and their process settings, materials, and geometrical design. The accuracy of identifying and analyzing these features becomes crucial in defining their relationship with manufacturing process variables. Usually, the best practice for defining surface quality is through parametric characterization, which provides a quantitative description of either the stochastic or deterministic nature of manufactured surfaces. However, the challenge with AM is that it generates surfaces that often contain both the aforementioned surface features, which make it particularly difficult to identify the manufacturing “footprints” through the parametric description. Therefore, the surface topography of AM may often require novel characterization methods to fully interpret the manufacturing process and thereby predict and optimize its product performance. The overall goal of this thesis is to provide an optimal approach toward the characterization of AM surfaces so that it gives a better understanding of the manufacturing process and also assists in process optimization to control the surface quality of the printed products. To realize this goal, the surface texture of AM processes was studied, particularly Material Extrusion (MEX), Vat Photopolymerization (VPP), and Powder Bed Fusion (PBF). These processes present topographical features that cover most of the surface scenarios in AM. Hence, to explain these varied surface features, a diverse range of surface characterization tools, such as Power Spectral Density (PSD), scale-sensitive fractal analysis, feature-based characterization, and quantitative characterization by both profile and areal surface texture parameters, were included in the analysis. Additionally, a methodology was developed using a statistical approach (linear multiple regression) and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces. Finally, the knowledge gained through the above-mentioned measurements and analysis is put to use to optimize the AM process to achieve enhanced surface quality. The results suggest that the developed approaches can be used as a guideline for AM users who are looking to optimize the process for gaining better surface quality and component functionality, as it works effectively in finding the significant parameters representing the unique signatures of the manufacturing process.