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The high frequency of accidents caused by drowsiness underscores the critical importance of implementing efficient systems for predicting drowsiness to protect people and avert possible catastrophe. This article presents a new method for forecasting drowsiness by utilizing advanced deep learning methods, such as Convolutional Neural Networks (CNNs). By utilizing improvements in computer vision and artificial intelligence, our planned system seeks to predict drowsiness accurately through the analysis of facial cues and expressions that signal fatigue. By combining a unique dataset containing a variety of facial pictures showing different levels of alertness and sleepiness, we create a strong training structure. The dataset includes samples of both closed and open eyes, yawning, and alertness, offering a detailed depiction of visual signs related to drowsiness. We use TensorFlow and Keras libraries to create and train a custom CNN model for predicting drowsiness. The suggested CNN design includes convolutional layers for extracting features, max-pooling layers for reducing dimensionality, and fully connected layers for classification. Methods like randomly flipping and rotating data are used to improve the model's ability to generalize and reduce overfitting. Moreover, incorporating batch normalization and dropout layers improves the robustness and stability of the model, making it more resistant to noise and data variability. We train the model on a carefully selected dataset and assess its performance using conventional measures including accuracy and loss. The model showed encouraging outcomes, reaching a validation accuracy of around 97.12% at the end of 41 epochs. By conducting thorough tests and confirming it, we establish the effectiveness and dependability of our suggested method in effectively identifying tiredness incidents. Our results highlight the capability of deep learning techniques in reducing the dangers linked to drowsiness-caused incidents. Our suggested system utilizes CNNs and takes advantage of facial expression analysis to provide an active method for predicting drowsiness in real-time. The consequences of this study apply to different areas like transportation safety, workplace productivity, and healthcare, involving drowsiness.