Decision-making driven by the CI (Computational Intelligence) has revolutionized healthcare, environmental sciences, finance, business management, robotics, and gaming. Computational Intelligence (CI) is a subfield of artificial intelligence that focuses on developing intelligent systems by mimicking natural biological processes. These processes include evolution and swarm behavior, utilizing techniques like neural networks, fuzzy logic, and evolutionary computation to solve complex problems. It might be difficult to tackle with traditional computing methods, which makes it highly relevant for applications requiring adaptive learning and decision-making in uncertain environments. Using AI for decision-making significantly increases accuracy efficiency, fastens decision-making, reduces biases, and helps in better predictive analysis and risk management. Artificial Intelligence is transforming disease diagnosis by analyzing large-scale medical data with the help of image analysis and pattern recognition to identify different diseases, which will further lead to making personalized treatment plans. AI models can also analyze patient data, identifying individuals at high risk of getting readmitted even after discharge and permitting healthcare providers to step in with preventative measures. Similarly, AI is crucial to ecosystem monitoring and management in environmental science. It analyses the data from sources like camera traps, satellite images and sensor networks, enabling faster and more accurate identification of environmental changes, threats, and trends. Conservation organizations utilize AI to analyze camera trap images to track individual elephants, monitor population dynamics, and identify poaching hotspots. Ethical considerations in environmental management are important for the responsible use of AI. This chapter delves into the implications of computational intelligence in the healthcare and environmental sciences. With the help of advanced techniques such as neural networks and fuzzy logic, CI not only addresses complex challenges but also leads to adaptive learning in unknown environments. The applications discussed in this chapter demonstrate how AI-driven systems can enhance disease diagnosis and ecosystem management. The insights of this chapter provide a foundation for understanding how CI can be used to navigate the complexities of modern society, paving the way for innovative solutions that enhance individual and collective well-being. The chapter also explains the real-world examples, like how neural networks help in diagnosing diseases through medical imaging, or how fuzzy logic and evolutionary algorithms support smarter farming and environmental decisions. These stories reflect both the powerful impact and the moral questions that come with including CI in our daily lives.