This paper introduces a generalized framework for \textbf{Partial Difference Equations (PE)} as a natural extension of classical difference equations to discrete spatiotemporal systems. We begin by defining and classifying PE, providing analytical solutions for several linear cases, and establishing foundational concepts such as shift operators, discrete Hilbert spaces, and discrete Green's functions. We then formulate a wide range of well-known models, including elementary cellular automata, Conway’s Game of Life, sandpile model, firefly synchronization model, Langton’s ant, forest-fire model, and the OFC earthquake model, within the language of PE. Novel nonlinear PE are also introduced to explore emergent complexity. A key theoretical advancement is the replacement of the classical “edge of chaos” notion with a refined concept called \textbf{quasichaos}, characterized by heterogeneous pattern formation, power-law statistics, and algorithmic universality. The final section extends PE to non-rectangular and topologically rich grids, such as hexagonal tilings, polyhedral complexes, and irregular networks, drawing analogies to PDE on manifolds. We conclude by classifying four modeling paradigms across discrete and continuous spacetime domains and suggest that PE offer a unifying language for simulating complex, self-organized, and biological systems.