This study presents a Poisson-based exceedance probability model rooted in stochastic process theory and statistical rate estimation to characterize earthquake occurrence in the Philippines using a PHIVOLCS-patterned seismic dataset. Earthquakes are modeled as realizations of a stationary Poisson counting process, where magnitude-threshold exceedances correspond to rare-event arrivals, analogous to demand or failure events in queueing and reliability systems. A 50-year catalogue comprising 5,895 events with magnitudes M ≥ 4.0 was analyzed using frequency–magnitude statistics to estimate intensity parameters for thresholds M ≥ 4.0, M ≥ 5.0, M ≥ 6.0, and M ≥ 7.0. These estimated rates were embedded in closed-form Poisson exceedance functions to compute probabilities at one-, ten-, and fifty-year horizons under standard assumptions of independence and temporal stationarity. The results yielded mean annual rates of 117.9, 14.04, 1.30, and 0.18 events for increasing magnitude thresholds, respectively. The exceedance probability of at least one M≥6.0 event was 72.7% within one year and approached unity over a decade, while M≥7.0 events exhibited a 16.5% annual exceedance probability that increased to over 99% within fifty years. From an operations research standpoint, these exceedance probabilities serve as inputs to stochastic optimization, enabling risk-informed decisions in capacity planning, resource allocation, and resilience optimization under uncertainty. The proposed framework demonstrates how classical stochastic models, widely used in queueing theory and reliability analysis, can be rigorously applied to seismic data, reinforcing the role of applied probability and statistics in interdisciplinary hazard modeling and decision science.