Traditional brain tumor detection methods suffer from subjectivity and unpredictability and have paved the way for a paradigm shift to artificial intelligence (AI) solutions. Utilizing computer vision, deep learning, and machine learning, AI algorithms enhance the precision of tumor identification through advanced imaging techniques such as computed tomography and magnetic resonance imaging. AI demonstrates unprecedented effectiveness and efficiency by deciphering diverse tumor types, detecting minute anomalies, and predicting patient outcomes. However, challenges persist, including the demand for high-quality annotated datasets, potential biases in training data, interpretability issues, and integration complexities within clinical workflows. This abstract underscores the transformative potential of AI, emphasizing the urgent need to surmount these challenges. It serves as a beacon, guiding the exploration of innovative approaches crucial for the seamless integration of AI into brain tumor diagnosis. In navigating the present landscape, addressing challenges, and envisioning future applications, this chapter provides a concise yet comprehensive roadmap for the future of brain tumor detection.