  Wireless Mesh Networks improve their capacities by equipping mesh nodes with
multi-radios tuned to non-overlapping channels. Hence the data forwarding
between two nodes has multiple selections of links and the bandwidth between
the pair of nodes varies dynamically. Under this condition, a mesh node adopts
machine learning mechanisms to choose the possible best next hop which has
maximum bandwidth when it intends to forward data. In this paper, we present a
machine learning based forwarding algorithm to let a forwarding node
dynamically select the next hop with highest potential bandwidth capacity to
resume communication based on learning algorithm. Key to this strategy is that
a node only maintains three past status, and then it is able to learn and
predict the potential bandwidth capacities of its links. Then, the node selects
the next hop with potential maximal link bandwidth. Moreover, a geometrical
based algorithm is developed to let the source node figure out the forwarding
region in order to avoid flooding. Simulations demonstrate that our approach
significantly speeds up the transmission and outperforms other peer algorithms.
