Using deep learning approaches, recognizing human actions from video sequences by automatically deriving significant representations has demonstrated effective results from unprocessed video information. Artificial intelligence (AI) systems, including monitoring, automation, and human-computer interface, have become crucial for security and human behaviour analysis. For the visual depiction of video clips during the training phase, the existing action identification algorithms mostly use pre-trained weights of various AI designs, which impact the characteristics discrepancies and perseverance, including the separation among the visual and temporal indicators. The research proposes a 3-dimensional Convolutional Neural Network and Long Short-Term Memory (3D-CNN-LSTM) network that strategically concentrates on useful information in the input frame to recognize the various human behaviours in the video frames to overcome this problem. The process utilizes stochastic gradient descent (SGD) optimization to identify the model parameters that best match the expected and observed outcomes. The proposed framework is trained, validated, and tested using publicly accessible UCF11 benchmark dataset. According to the experimental findings of this work, the accuracy rate was 93.72%, which is 2.42% higher compared to the state-of-the-art previous best result. When compared to several other relevant techniques that are already in use, the suggested approach achieved outstanding performance in terms of accuracy.