This study investigates the performance of three dynamic scheduling approaches—proactive, reactive, and STNU-based—for solving the Multi-Mode Resource-Constrained Project Scheduling Problem with maximal time-lags and no-wait constraints (MMRCPSP/max) in uncertain environments. The performance of the approaches is validated on three key performance measures: solution quality (makespan), offline computation time, and online computation time. Drawing from the current work on stochastic RCPSP/max, this research introduces a more realistic formulation of the problem with multi-mode execution, generalised time lags, and no-wait constraints, under the PyJobShop library and stochastic duration modelling. Experimental results, based on altered PSPLIB instances, show that the three algorithms yield similar feasibility rates. However, they show distinct trade-offs in terms of solution quality, time offline, and time online. The proactive algorithm requires the shortest offline and online computational times, but provides slightly worse makespans. The STNU-based algorithm produces the best average solution quality, with significantly higher offline time. The reactive algorithm offers competitive offline times and solution quality, but has the largest online computational time. The findings provide insight into the trade-offs in solution quality and computational time in dynamic project scheduling and offer insight for using the strategies in the real world.