Neural network models have revolutionized action recognition in videos, enabling precise and efficientprocessing of complex visual data. These AI-powered tools mimic or even surpass human abilities in understandingvisual information. Convolutional Neural Networks (CNNs) excel at extracting spatial featuresfrom individual frames, making them ideal for analyzing intricate scene details. Recurrent Neural Networks(RNNs), particularly LSTMs, add a temporal dimension, capturing movement sequences coherently.To enhance accuracy, Two-Stream Networks were developed, combining static frame analysis with dynamicmotion flows to better interpret scenes. Additionally, 3D Convolutional Networks (C3D) treat videos asintegrated spatiotemporal units, advancing comprehensive video analysis .These models are widely usedin applications like human activity recognition and security surveillance. Their goal is to identify sequentialactions, classify them into categories, and map them to predefined event classes. This research examinesneural network models for video action recognition, comparing their accuracy, strengths, weaknesses,and proposing improvements. Key findings highlight CNNs’ strength in spatial feature extraction butnote limitations in handling temporal dynamics. RNNs and LSTMs address this gap but may struggle withlong-term dependencies. Two-Stream Networks and C3D models offer robust solutions by integrating spatialand temporal data but require significant computational resources .Based on testing results, a guidancesystem is proposed to help users select the most suitable model based on video type. For instance,CNNs are recommended for detailed frame analysis, while LSTMs or C3D models suit videos with complexmotion patterns. This approach ensures optimal performance tailored to specific classification needs.Keywords: Deep Learning, Convolutional Neural Networks CNN , Recurrent Neural Networks (RNN) ,Long Short-Term Memory (LSTM) , 3D Convolutional Networks (C3D) , Two-Stream Network , Transformer-Based Models for Video.