Electrical Impedance Spectroscopy (EIS) has emerged as a promising non-destructive technique for real-time food quality and safety assessment. The optimization of EIS involves fine-tuning various parameters such as frequency range, electrode configuration, and data processing algorithms to enhance sensitivity, accuracy, and speed in detecting food quality attributes and contaminants. This research explores the optimization of EIS through the integration of advanced signal processing techniques, machine learning models, and hardware innovations. The study begins by analyzing the fundamental principles of EIS and identifying critical factors affecting its performance in food assessment applications. A systematic approach is employed to optimize frequency range selection, ensuring that the most informative spectral features are captured. Additionally, the study evaluates different electrode configurations to improve contact quality and minimize noise, leading to more reliable measurements. Advanced machine learning algorithms are incorporated to process the impedance data, enabling real-time classification and prediction of food quality parameters such as freshness, moisture content, and microbial contamination. The research also explores the miniaturization of EIS devices, aiming for portable and cost-effective solutions suitable for on-site applications in food processing and distribution. The results demonstrate significant improvements in the accuracy, speed, and reliability of EIS for food quality and safety assessment, highlighting its potential as a key tool in modern food monitoring systems. This study paves the way for the widespread adoption of optimized EIS technology, contributing to enhanced food safety, reduced waste, and better consumer protection. Future work will focus on expanding the range of detectable food attributes and integrating EIS with other sensor technologies for comprehensive food quality monitoring.