The translation of mathematical theory into reliable computational practice represents a central challenge in applied mathematics, requiring rigorous attention to stability, convergence, and algorithmic efficiency across diverse application domains. This review examines the foundational principles that govern this translation, tracing the pathway from abstract mathematical formulations through numerical discretization to practical implementation on modern computing architectures. The exposition begins with an overview of classical numerical frameworks—finite difference, finite element, and spectral methods—elucidating their theoretical underpinnings through consistency analysis, variational principles, and approximation theory. The discussion progresses to advanced computational techniques including mesh-free methods, multigrid solvers, and adaptive strategies that address the limitations of traditional approaches when confronted with complex geometries or multiscale phenomena. Central to the review is a rigorous treatment of stability and convergence theory, encompassing the Lax equivalence theorem for linear problems, energy methods for parabolic and hyperbolic systems, and nonlinear stability concepts including entropy conditions and monotonicity preservation. Error estimation frameworks—both a priori and posteriori—are examined in the context of adaptive refinement and solution verification. Computational efficiency considerations spanning sparse matrix technologies, preconditioning techniques, and parallel algorithms are analyzed for their impact on large-scale simulation capability. Applications in structural mechanics, fluid dynamics, and computational science illustrate the practical realization of these theoretical constructs. The review concludes by identifying persistent challenges and emerging directions, including multiscale coupling, uncertainty quantification, and structure-preserving algorithms that define the frontier of numerical innovation in applied mathematics.