We investigate the nature of probability and the possibility of prediction within the framework of Universal Modular Dynamics (UMD), where physical structure is defined in terms of the density operator ρ, its modular generator K = −log ρ, and the associated spectral distribution p(k). In contrast to conventional approaches, where probability is introduced as a primary concept, we develop a structural interpretation in which probability emerges as a derived property of the underlying organization. We show that the probability of a state is determined by the interplay of three factors: its structural weight within p(k), its accessibility to the observer, and its dynamical stability. This leads to the interpretation of probability as a measure of structural realizability. Within this framework, prediction is not understood as the determination of specific future events. Instead, it corresponds to the analysis of the spectrum of possible states and their relative stability under structural evolution. We demonstrate that observed outcomes arise from the competition between structurally allowed configurations, constrained by accessibility and stabilized by dynamics. This provides a unified description of probability, transitions, and observable behavior without introducing probability as an independent postulate. The results establish a consistent framework in which probability and prediction are derived from structure, and suggest that complex systems may be analyzed in terms of their accessible and stable configurations, rather than through direct event-based forecasting.