Broadcasting is an essential and effective data propagation mechanism with several important applications, such as route discovery, address resolution and many other network services. Though data broadcasting has many advantages, it can also cause a high degree of contention, collision and congestion, leading to what is known as 'broadcast storm problems'. Broadcasting has traditionally been based on the flooding protocol, which simply overflows the network with a high number of rebroadcast messages until these reach all the network nodes. A good probabilistic broadcast protocol can achieve high saved rebroadcast (SRB), low collision and a lower number of relays. When a node is in a sparse region of the network, rebroadcasting is relatively more important while the potential redundancy of rebroadcast is low because there are few neighbours which might rebroadcast the packet unnecessarily. Further, in such a situation, contention over the wireless medium resulting from Redundant broadcasts is not as serious as in scenarios with medium or high density node populations. This research proposes a dynamic probabilistic approach that dynamically fine-tunes the rebroadcast probability according to the number of neighbouring nodes distributed in the ad-hoc network for routing request packets (RREQs) without requiring the assistance of distance measurements or location-determination devices. The main goal of this approach is to reduce the number of rebroadcast packets and collisions in the network. The performance of the proposed approach is investigated and compared with simple AODV, fixed-probabilistic and adjusted-probabilistic flooding [1] schemes using the GloMoSim network simulator and a number of important MANET parameters, including node speed, traffic load and node density under a Random Waypoint (RWP) mobility model. Performance results reveal that the proposed approach is able to achieve higher SRB and less collision as well as a lower number of relays than fixed probabilistic, simple AODV and adjusted-probabilistic flooding. In this research, extensive simulation experiments have been conducted in order to study and analyse the proposed dynamic probabilistic approach under different mobility models. The mobility model is designed to describe the movement pattern of mobile customers, and how their position, velocity and acceleration change over time. In this study, a new enhanced dynamic probabilistic flooding scheme is presented. The rebroadcast probability p will be calculated dynamically and the rebroadcasting decision will be based on the average number of nodes in the ad-hoc networks. The performance of the new enhanced algorithm is evaluated and compared to the simple AODV, fixed-probabilistic, adjusted-probabilistic and dynamic-probabilistic flooding schemes. It is demonstrated that the new algorithm has superior performance characteristics in terms of collision, relays and SRB. Finally, the proposed schemes are tested and evaluated through a set of experiments under different mobility models to demonstrate the relative merits and capabilities of these schemes.