  High sensitivity detection plays a vital role in science discoveries and
technological applications. While intriguing methods utilizing collective
many-body correlations and quantum entanglements have been developed in physics
to enhance sensitivity, their practical implementation remains challenging due
to rigorous technological requirements. Here, we propose an entirely
data-driven approach that harnesses the capabilities of machine learning, to
significantly augment weak-signal detection sensitivity. In an atomic force
sensor, our method combines a digital replica of force-free data with anomaly
detection technique, devoid of any prior knowledge about the physical system or
assumptions regarding the sensing process. Our findings demonstrate a
significant advancement in sensitivity, achieving an order of magnitude
improvement over conventional protocols in detecting a weak force of
approximately $10^{-25}~\mathrm{N}$. The resulting sensitivity reaches $1.7(4)
\times 10^{-25}~\mathrm{N}/\sqrt{\mathrm{Hz}}$. Our machine learning-based
signal processing approach does not rely on system-specific details or
processed signals, rendering it highly applicable to sensing technologies
across various domains.
