Transporting raw data which is usually correlated over multi-hop wireless links can be costly both in terms of time as well as resources. This is likely to be a pressing problem particularly in the emerging Machine-to-Machine (M2M) communication paradigm wherein the amount of data is expected to proliferate. With in-network processing and data aggregation, the total amount of forwarding traffic on multi-hop routes can be significantly reduced by pre-processing of correlated information. However, a suitable data forwarding scheme is needed as a prior condition in order to efficiently compute and relay data. By taking advantage of distributed processing, this paper provides a content centric and load balancing aware distributed data routing solution for large-scale multi-hop M2M wireless networks. Independent routing decisions are made by each node to refine its next hop data forwarding node selection using only local information. Hence, it is highly adaptive to dynamic environments. A hybrid objective function is proposed for route selection which includes two main parts: 1) reduce the communication traffic by aggregating similar type of data, hence increasing the processing gain; 2) balance the energy-consumption among neighbouring nodes taking into account heterogeneous node residual energy levels so as to reduce both computation and communication costs and at the same time avoid early energy depletion of hot-spot nodes. We show that the proposed algorithm is able to extend the network lifetime and provides significant energy saving in complex large-scale Multihop wireless networks.