We present DAMPC, a distributed, adaptive-horizon and adaptive-neighborhood algorithm for solving the stochastic reachability problem in a distributed-flocking modeled as a Markov decision process. At each time step, DAMPC takes the following actions: First, every agent calls a centralized, adaptive-horizon model-predictive control AMPC algorithm to obtain an optimal solution for its local neighborhood. Second, the agents derive the flock-wide optimal solution through a sequence of consensus rounds. Third, the neighborhood is adaptively resized using a flock-wide, cost-based Lyapunov function V. This improves efficiency without compromising convergence. The proof of statistical global convergence is non-trivial and involves showing that V follows a monotonically decreasing trajectory despite potential fluctuations in cost and neighborhood size. We evaluate DAMPC's performance using statistical model checking. Our results demonstrate that, compared to AMPC, DAMPC achieves considerable speed-up (2 in some cases) with only a slightly lower rate of convergence. The smaller average neighborhood size and lookahead horizon demonstrate the benefits of the DAMPC approach for stochastic reachability problems involving any distributed controllable system that possesses a cost function.