Analog and radio-frequency circuit design requires extensive exploration of both circuit topology and parameters to meet specific design criteria. This design process is highly specialized and time-intensive, particularly as the number of circuit parameters increases and the circuit becomes more complex. Prior research has explored the potential of machine learning to enhance circuit design procedures; however, primarily focused on simple circuits. To date, a generic and diverse dataset with robust metrics on advanced mm-wave circuits does not exist. To bridge this gap, we present AICircuit, a comprehensive multi-level dataset and benchmark for developing and evaluating ML algorithms in analog and radio-frequency circuit design. AICircuit comprises seven commonly used advanced analog circuits and two complex wireless transceiver systems composed of multiple circuit blocks, encompassing a wide array of design scenarios encountered in real-world applications. We extensively evaluate various ML algorithms on the dataset, revealing the potential of ML algorithms in learning the mapping from the design specifications to the desired circuit parameters.