The inter-neural connectivity plays a major role in the various regions of the brain affecting their functionality. In this regard, the knowledge of the dendritic interconnect structure is imperative. There are quite a few experimental approaches currently available to explore the dendritic connectivity which have their own shortcomings. On the other hand, a theoretical approach would be helpful in developing simulation and fault models for the dendritic tree structure which has wide applications in research and diagnostic purposes. In this paper, we propose a powerful approach based on a randomized algorithm, to predict the dendritic tree structure connectivity. This approach involves two finite steps, one dealing with the topological aspects and the other dealing with the fixing up of the physical length of each of the dendritic branches. The randomized algorithm employed is the simulated annealing [kigv83] and it is powerful in the sense that it can overcome local minima to achieve global minima to obtain near optimal solution. We also propose in this paper an integrated approach in which simulated annealing [kigv83] algorithm is simultaneously employed on both the dendritic structure prediction and the length prediction with dynamic scheduling [vanaa87]. This ensures faster convergence towards optimal solution. Extensive simulation have been carried out and results are presented demonstrating the efficacy of the method.