<p>This article presents a systematic review based on PRISMA methodology with 38 main studies (2000–2024) of academic databases and we analyze the evolution, challenges, and opportunities of ambient intelligence (AmI) in the 21st century. AmI is defined as the capability of environments to interact intelligently and adaptively through sensors and devices that respond effectively to the presence and needs of users. Key real-world applications include: Healthcare (remote monitoring of chronic patients and prediction of behavior in dementia care), Home automation (adaptive lighting and temperature systems), and Security (intruder detection and NFC-based access control system). The taxonomy of AmI systems comprises three phases: (i) IoT based sensing, (ii) AI-driven reasoning-actuation, and (iii) human–computer interaction interfaces, to create an intelligent and adaptive environment. Also, the computational intelligence (or supervised learning models) is addressed highlighting widely used classification algorithms such as decision trees, Naïve Bayes, k-nearest neighbors, Support Vector Machines, and Artificial neural networks. It’s important to highlight that computational intelligence models aren’t the same as AmI systems. Although these models are crucial for making data-driven decisions, they require significant preprocessing such as cleaning up data noise or normalizing it to function properly within AmI environments. This article provides a comprehensive overview of the current state of AmI, addressing both opportunities and challenges. Ethical and social considerations such as privacy, autonomy, and economic scalability are critical factors for AmI’s widespread acceptance. Future research directions emphasize scalable architectures, interdisciplinary collaboration (e.g., AI ethics), and secure edge computing, aligning with recent technological advances.</p>