The rising demand for orthopedic implants, driven by rising life expectancy and medical advancements, highlights the need for improved implant-tissue integration. Initial cellular interactions, such as adhesion, migration, and differentiation, are critical for implant success, with surface topography playing a key role. However, existing methods for analyzing focal adhesions (FAs) and cell behavior are inconsistent, often relying on manual or proprietary tools that limit reproducibility. This study addresses these limitations by developing an automated, open-source, and multiparametric workflow for high-throughput 3D analysis of cell morphology and FA dynamics, enabling standardized quantification of cellular responses to biomaterial surfaces. This project aimed to create such a workflow for analysis of cellular characteristics. It was then applied to study cell behavior on titanium surfaces, polished titanium (pTi) and dry-etched titanium (deTi). After cell culturing, fixation, immunostaining, high-resolution imaging using spinning disk confocal microscopy was used to quantify parameters, this included cell and nucleus area, aspect ratio (AR), nucleus height, volume, and FA characteristics (area and number) using FIJI/ImageJ. Validation against manual quantification confirmed its accuracy and efficiency, reducing analysis time by at least fourfold. This workflow was applied to analyze MC3T3-E1 preosteoblasts and human mesenchymal stem cells (hMSCs) on pTi and deTi surfaces. Results showed distinct differences, with hMSCs demonstrating greater adaptability to rougher deTi surfaces, while MC3T3-E1 cells remained more confined. Statistical analysis via two-way ANOVA confirmed surface type as the primary factor influencing cell behavior, with time and surface-time interactions varying in significance across parameters. These results suggested that hMSCs adapt more easily to rougher surfaces, as evidenced by increased spreading, more organized actin structures, and distinct FA formation, highlighting their potential for biomaterial applications due to their ability to dynamically remodel their shape and focal adhesions, which may enhance mechanosensing and tissue integration. The developed workflow offers a valuable tool for advancing research into biomaterial cell interactions and optimizing implant design for improved osseointegration. Additionally, it can be expanded to investigate a broader range of biomaterials and cell types, further enhancing its applicability in tissue engineering and regenerative medicine.