In the case of a graphical model, machine learning algorithms used to evaluate a query can be broadly classified into exact and approximate inference algorithms. Exact inference algorithms use only network parameters to evaluate a query. However, these algorithms are typically intractable on large networks due to exponential time and space complexity. Approximate inference algorithms are widely used in practice to overcome this constraint, with a trade-off in accuracy. It includes sampling and propagation-based algorithms. These approximate algorithms may also suffer from scalability issues if applied on large networks, for achieving higher accuracy. To address this challenge, we have designed and implemented several MapReduce-based distributed versions of a specific type of approximate inference algorithm called Adaptive Importance Sampling (AIS). We compare and evaluate the proposed approaches using benchmark networks. Experimental results show that our proposed approaches achieve significant scaleup and speedup compared to the sequential method, while achieving similar accuracy asymptotically.