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The latest standard for video coding is versatile video coding (VVC) / H.266 which is developed by the joint video exploration team (JVET). Its coding structure is a multi-type tree (MTT) structure, which consists of two types of trees that are Ternary Tree (TT) and Binary Tree (BT). Due to the use of brute force quest for residual rate-distortion the quad-tree and multi-type tree (QTMT) structure of the coding unit (CU) split and contributes over 98% of the encoding time. This structure is efficient in coding, however, increases computational complexity. The current paper proposes a deep learning technique to predict the QTMT based CU split rather than just the brute-force QTMT method to substantially speed up the time of the encoding process for VVC/H.266 intra mode. In the first phase, we developed an extensive database containing ample CU splitting patterns and various streaming videos. In the second phase, we suggest a multi-level exit CNN (MLE-CNN) model with a redundancy removal mechanism at different levels to determine the CU partition. In the third phase, for the training of MLECNN model we have established the adaptive loss function and analyzing the both unknown number of partition modes and the focus on RD cost minimization. Finally, a variable threshold decision system is established to achieve the targeted low complexity and RD performance. Ultimately experimental findings show that VVC/H.266 encoding time has reduced to 69.11% from 47.91% with insignificant bjontegaard delta bit rate (BDBR) to 2.919% from 1.023% which performs better than the existing futuristic and modern approaches.