This research pioneers the development of an innovative approach for refining dental implant fixture surfaces using the SLA+Anodizing method. Leveraging a rich dataset encompassing 68 distinct implant surface treatment states, the study employs an Artificial Neural Network (ANN) to predict crucial parameters such as surface roughness and cellular viability. Through meticulous training and validation, the ANN demonstrates a remarkable 3% error rate in comparison to experimental results, underscoring its precision. The methodology extends beyond prediction, facilitating the optimization of implant surfaces for enhanced osseointegration. Experimental validation, including Atomic Force Microscopy and Molecular Cytotoxicity Tests, corroborates the accuracy of the ANN predictions. The study pioneers a transformative era in dental implantology, introducing a tailored and adaptable approach that bridges gaps in understanding the intricate interplay between surface modifications and biological responses. This work sets the stage for a paradigm shift in dental science, emphasizing precision, personalization, and elevated standards of care.