As many new coding techniques and coding structures have been introduced to further improve the coding efficiency of depth maps in 3D video extensions, the coding complexity has been greatly increased. Fast algorithms are now needed to improve coding unit (CU) depth decisions as well as the coding pattern decision based on the coding. This paper presents an innovative machine learning-based approach aimed at mitigating the complexity associated with in-frame coding algorithms. We build different clustering models for different CU sizes to cluster CUs of the same size to decide their CU sizes. This is achieved by augmenting ensemble clustering through the expedited propagation of clustering similarities, considering CU with the same or similar texture complexity the same as for CU depth selection, which is informed by a comprehensive analysis of the original texture and its neighboring elements. The experimental findings demonstrate that the proposed scheme yields a substantial average reduction of 44.24% in the coding time. Remarkably, the corresponding Bjøntegaard delta bit rate (BDBR) increment observed for the synthetic view is a mere 0.26%.