Aging is characterized by the progressive deterioration of cellular and tissue homeostasis, driven by interconnected hallmarks including mitochondrial dysfunction, reactive oxygen species (ROS) accumulation, telomere attrition, cellular senescence, inflammaging, genomic instability, and epigenetic dysregulation. This theoretical framework integrates biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology to model synergistic bio- and non-biochemical interventions aimed at cellular and tissue rejuvenation.We develop stochastic differential equation (SDE) models to capture the dynamics of these hallmarks, with a focus on the biphasic progression of epigenetic drift: an initial programmed phase (from birth to approximately age 45) marked by linear methylation changes, succeeded by midlife stochastic acceleration at CpG sites, as evidenced by a bifurcation characterized by a positive Lyapunov exponent (\(\mu_L \approx 0.012 \, \mathrm{yr}^{-1}\)). These models employ Itô semimartingales with positivity constraints and are calibrated using multi-omics datasets from NHANES, UK Biobank, GTEx, and TCGA cohorts.Key innovations encompass: (1) biphasic epigenetic SDEs incorporating hierarchical noise structures; (2) hybrid SDE-agent-based multiscale simulations; (3) extended Fourier amplitude sensitivity testing (eFAST) for Sobol indices; (4) No-U-Turn Sampler (NUTS) for Bayesian inference; and (5) physics-informed neural networks (PINNs) as surrogate models. Interventions, such as liposomal nicotinamide mononucleotide (NMN) and fisetin, are simulated using Godunov finite volume methods for partial differential equations (PDEs).Findings reveal pronounced synergies, wherein combined therapies yield hallmark attenuations comparable to a projected 2.5-year reduction in epigenetic age post-bifurcation, corroborated by preclinical and early-phase clinical data. Spatial analyses demonstrate diminished epigenetic clustering (Moran's \(I\) reduced from 0.42 to 0.11). Translational extensions leverage AI-driven digital twins for personalized prognostication and CRISPR-AI-guided gene therapy (with off-target effects \(<0.1\%\)). Insights from ongoing clinical trials (e.g., NCT04910061) and ethical considerations (Gini coefficient \(<0.2\)) are incorporated, supported by replicable code and comprehensive stability analyses.