Aquatic ecosystem monitoring is important for supporting biodiversity and environmental stability, yet it faces increasing threats from pollution, climate change and human activities. This study presents the development and deployment of a low-cost multi-sensor data logging system for real-time monitoring of Lagos Lagoon. The system integrates temperature sensors, hydrophones, and imaging devices to collect environmental data. Results showed that temperature variations ranged from ~28.5 to 31.5 °C, with fluctuations influenced by partial and full submersion. Acoustic analysis revealed dominant frequencies below 500 Hz, indicative of biological and anthropogenic activity in the lagoon. Machine learning models trained on 31 species closely agreed with the environmental dataset despite some noticeable deviations, suggesting potential improvements through data augmentation and model refinement. Despite challenges such as signal attenuation in submerged conditions and image degradation due to water turbidity, the system successfully recorded and logged environmental parameters. This study demonstrates the feasibility of using artificial intelligence-powered, cost-effective sensor technology for continuous aquatic monitoring, with implications for biodiversity conservation and water resource management. Future research should focus on enhancing wireless communication, refining species detection algorithms and improving sensor resilience in harsh aquatic conditions.