Abstract: We used the seismic regularity and seismic strain dynamics coefficients to investigate the seismic process preceding the 2025 M7.7 Myanmar earthquake, which ruptured the central portion of the Sagaing Fault. The analysis focused on a seismic cluster located between the Sagaing Fault and Sunda Trench over the ten years prior to the mainshock. In our approach, earthquakes are parameterized by magnitude, elapsed time since the previous event, and epicentral distance to the previous event. These parameters were transformed into equivalent dimensions, ensuring their comparability. The seismic regularity coefficient, which is the mean distance between earthquakes represented by these transformed parameters, acts as a proxy for the strain localization and rupture coherence. The seismic strain dynamics coefficient, which is defined as the logarithm of the ratio between the sum of the cube roots of the scalar seismic moments and the time span over which the events occurred, serves as a proxy for the average inelastic strain accumulation within the fault damage zone. Approximately 29 months before the mainshock, a precursory signal emerged in the seismic regularity coefficient time series. This signal decreased to a pronounced minimum approximately 26 months before the earthquake, followed by an increase to a distinct maximum approximately 10 months before the mainshock. The seismicity responsible for this pattern was distributed across a large area (~1,550 km × 500 km), with most events occurring on smaller fault structures west of the Sagaing Fault, rather than along the main fault itself. The premonitory behavior of the seismic regularity coefficient prior to the Myanmar earthquake closely matched the patterns observed during other large events. The signal was even more pronounced when analyzed with the seismic strain dynamics coefficient. The joint evolution of both parameters was interpreted within the framework of a model that describes the preparatory process leading to large earthquakes. The obtained results confirm the potential of this approach for long-term earthquake forecasting.