Photonic reservoir computing is a promising paradigm for large-scale classification and prediction problems. However, its single-layer nature is a bottleneck for higher performance and accuracy. This paper proposes MReC, a novel multi-layer photonic reservoir computing architecture made-up of photonic components such as Mach-Zehnder interferometer and optical fiber spool. The proposed design involves a deep pipelined architecture and has been synthesized using commercial photonic CAD tools. System-level simulation of large-scale tasks like spoken digit recognition, channel equalization, and time-series prediction on MReC demonstrates an accuracy improvement up to 50% compared to the state-of-the-art photonic architecture in the literature. Further results indicate a power reduction up to 34% and at least 132x speedup when compared with the best reported results in the literature at a cost of 12% area overhead.