The Scanning Probe Microscopy (SPM) will accelerate materials discovery by systematically exploring combinatorial libraries, enabling rapid identification and optimization of novel materials. By transitioning to smaller scales and faster times, SPM can precisely manipulate and analyze materials uncovering new properties and behaviors that were previously inaccessible. This capability allows for real-time, high-throughput experimentation and iterative feedback, significantly speeding up the discovery process. The integration of advanced machine learning algorithms further enhances this process by automating decision-making and interpretation, leading to faster, more efficient exploration and innovation in materials science. Our goal is to combine the ML-driven operation with quantitative SPM methods including Piezoresponse Force Microscopy, Electrochemical Strain Microscopy, and photo assisted Kelvin Probe Force Microscopy