Antibiotic contamination in natural and engineered water systems has emerged as a critical environmental and public health concern, driving the need for fast, reliable, and on-site detection strategies. This work presents a new approach that combines machine learning with electrochemical sensing to enable real-time monitoring of antibiotic residues in water. By coupling data-driven analytical models with engineered electrode materials and an optimized sensing platform, the system achieves improved sensitivity, specificity, and detection limits over traditional analytical methods. Electrochemical signal profiles are used to train supervised learning algorithms capable of accurately identifying and quantifying multiple antibiotic compounds, allowing automated interpretation without expert intervention. The resulting sensor device is portable and cost-efficient, supporting broad deployment for routine environmental surveillance. Experimental studies confirm the strong performance of the integrated system, demonstrating its promise for water quality assessment and compliance monitoring. Overall, this research highlights how machine learning can significantly enhance electrochemical sensor technologies for environmental applications.