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The accurate estimation of human poses is a fundamental problem in computer vision, with applications ranging from action recognition to human-computer interaction. This research explores the enhancement of human pose estimation accuracy through the integration of Darknet-53, a deep convolutional neural network, and Bidirectional Long Short-Term Memory (BiLSTM) networks. The proposed approach is evaluated on benchmark datasets, demonstrating significant improvements in human pose estimation accuracy. This paper provides a comprehensive analysis of the methodology and experimental results, highlighting the effectiveness of the Darknet-53 and BiLSTM combination.