In recent years, the volume of data generated daily from various sources such as social networks, sensors, financial transactions, and hospital records has increased exponentially. This enormous amount of data is known as “big data,” which requires advanced tools and techniques to extract valuable information and make accurate decisions. Deep learning emerges as one of the most important tools, relying on multi-layered artificial neural networks to analyze data and recognize complex patterns within it. Deep learning techniques provide the ability to process massive and diverse datasets quickly and accurately, surpassing traditional methods, and enable forecasting future trends, analyzing behaviors, and uncovering hidden patterns. It is applied across multiple domains such as finance, healthcare, energy, marketing, and cybersecurity, contributing to efficiency improvement and productivity enhancement. One of the prominent applications in big data analysis is predicting product and service demand, detecting financial fraud, analyzing medical images and videos, and classifying unstructured data. These systems rely on vast amounts of data to train models, increasing accuracy and reducing errors. Deep learning is also used to analyze social media texts and conversations to detect sentiments and general trends, helping organizations understand their audience and make better marketing and strategic decisions. Moreover, deep learning improves the quality of digital recommendations, such as in e-commerce platforms and digital content services. Despite the powerful capabilities, challenges exist related to data volume, energy consumption, training time, and the need for advanced technical expertise to design and analyze models. Nevertheless, integrating deep learning with big data represents a revolution in how organizations handle information, enabling faster and more accurate decision-making. Investing in robust data infrastructure and utilizing deep learning techniques provides new opportunities for innovation and growth across sectors, positioning organizations competitively in the digital age. In the future, deep learning techniques are expected to become an essential part of big data analysis strategies, providing precise insights and predicting economic, social, and technological changes before they occur. Therefore, combining big data with deep learning is one of the most important modern tools to support decision-making and drive scientific and technological advancement while emphasizing the need for ethical and technical policies to ensure responsible and safe use of these technologies.