Background: Dental implant longevity depends on the complex interaction between mechanical stability and biological osseointegration. While finite element analysis (FEA) provides valuable mechanical insight, it remains static and computationally expensive. Objective: This study presents a hybrid time-resolved computational framework combining finite element data, osseointegration dynamics, and deep learning to predict the biomechanical behavior of titanium dental implants throughout the healing process. Methods: A parametric 3D FEA model simulated 800 implant–bone configurations varying in geometry, loading, and bone quality. A mechanobiological model of osseointegration described the monthly evolution of bone density, bone–implant contact (BIC), and interfacial stiffness over 12 months. These temporal variables were integrated into a hybrid Multilayer Perceptron – Long Short-Term Memory (MLP–LSTM) neural network — designed to capture both spatial and time-dependent features—trained to predict von Mises stress (σVM), maximum displacement (δmax), and fatigue safety factor (FSF, an indicator of long-term structural failure risk). Results: The model achieved R2 > 0.98 for all outputs and mean absolute errors <0.015. Temporal simulation revealed that interfacial stiffness increased by 270 %, while FSF declined nonlinearly with load above 200 N. Predictions were generated in <0.01 s per case, offering >4000 ×speed-up compared to conventional FEA. Conclusions: The framework captures both mechanical and biological evolution of the implant–bone interface, providing physiologically realistic, computationally efficient predictions. This approach represents a step toward personalized, AI-assisted implant design and load management. Clinically, this tool allows for rapid pre-surgical screening of implant designs against patient-specific risk factors. Limitations include the reliance on synthetic data derived from simplified bone geometries, static loading assumptions, and unvalidated mechanobiological parameters, necessitating future in vivo validation. These findings represent a computational proof-of-concept and require validation against patient-specific geometries and biological data before clinical adoption.