Deep learning has shown significant potential in improving the classification of exotic particles in high-energy physics. Traditional methods rely on shallow machine learning models with limited capacity to learn complex nonlinear functions, often requiring manual feature engineering. However, deep learning models can automatically learn complex functions and better distinguish between signal and background classes without manual input. This study demonstrates that deep learning methods can improve classification metrics by up to 8% over current approaches, highlighting their effectiveness in collider searches for exotic particles. High-energy physics aims to understand the fundamental properties of the universe by studying the elementary constituents of matter. Particle collisions at high-energy colliders produce exotic particles, which are rare and require advanced statistical methods for detection. Machine learning tools are crucial for analyzing the large volumes of data generated, as they can efficiently process and classify the data. The key challenge in particle detection is distinguishing between signal and background events. The relative likelihood function, which measures the ratio of likelihoods under different hypotheses, is essential for this task. However, this function is often complex and requires simulation for approximation. Machine learning techniques, such as neural networks, are used for dimensionality reduction and classification. Deep learning models, particularly deep neural networks (DNs), have shown promise in overcoming the limitations of shallow models. They can automatically learn high-level features from raw data, improving classification accuracy. This study compares deep learning with traditional methods on benchmark datasets for Higgs bosons and supersymmetric particles, demonstrating that deep learning achieves better performance. The results show that deep learning models, such as five-layer neural networks, outperform traditional shallow networks in classification tasks. They achieve higher area under the curve (AUC) values, indicating better discrimination between signal and background events. Additionally, deep learning models can automatically discover the insights contained in high-level features, leading to improved performance. The study also highlights the importance of using deep learning in high-energy physics, where data is limited and expensive. By improving classification accuracy, deep learning can enhance the discovery potential of particle physics experiments. The results suggest that deep learning techniques can significantly advance the field by automatically learning complex features and improving the detection of rare particles.Deep learning has shown significant potential in improving the classification of exotic particles in high-energy physics. Traditional methods rely on shallow machine learning models with limited capacity to learn complex nonlinear functions, often requiring manual feature engineering. However, deep learning models can automatically learn complex functions and better distinguish between signal and background classes without manual input. This study demonstrates that deep learning methods can improve classification metrics by up to 8% over current approaches, highlighting their effectiveness in collider searches for exotic particles. High-energy physics aims to understand the fundamental properties of the universe by studying the elementary constituents of matter. Particle collisions at high-energy colliders produce exotic particles, which are rare and require advanced statistical methods for detection. Machine learning tools are crucial for analyzing the large volumes of data generated, as they can efficiently process and classify the data. The key challenge in particle detection is distinguishing between signal and background events. The relative likelihood function, which measures the ratio of likelihoods under different hypotheses, is essential for this task. However, this function is often complex and requires simulation for approximation. Machine learning techniques, such as neural networks, are used for dimensionality reduction and classification. Deep learning models, particularly deep neural networks (DNs), have shown promise in overcoming the limitations of shallow models. They can automatically learn high-level features from raw data, improving classification accuracy. This study compares deep learning with traditional methods on benchmark datasets for Higgs bosons and supersymmetric particles, demonstrating that deep learning achieves better performance. The results show that deep learning models, such as five-layer neural networks, outperform traditional shallow networks in classification tasks. They achieve higher area under the curve (AUC) values, indicating better discrimination between signal and background events. Additionally, deep learning models can automatically discover the insights contained in high-level features, leading to improved performance. The study also highlights the importance of using deep learning in high-energy physics, where data is limited and expensive. By improving classification accuracy, deep learning can enhance the discovery potential of particle physics experiments. The results suggest that deep learning techniques can significantly advance the field by automatically learning complex features and improving the detection of rare particles.