This review explores the integration of machine learning (ML) and deep learning (DL) algorithms with sensing technologies to enhance sensor performance, accuracy, sensitivity, and adaptability. The core of this advancement lies in the fusion of AI with sensor technology, enabling efficient algorithms that drive improvements in device performance and novel applications in biomedical and engineering fields. The review discusses the impact of ML/DL on sensor design, calibration and compensation, object recognition, and behavior prediction, highlighting their potential to significantly upgrade sensor functionalities and widen their application range. It also addresses challenges in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements. ML/DL algorithms assist in sensor design by enabling inverse design methods, such as using artificial neural networks to design target sensor configurations based on desired performance outcomes. They also optimize sensor performance during the design process by addressing issues like small measurement ranges, low signal-to-noise ratios, and inadequate precision. For example, a refined method using a functional link artificial neural network (FLANN) estimates unknown coefficients in a power series expansion, capturing the sensor's nonlinear response. Additionally, ML/DL algorithms improve device adaptability across various environmental conditions, as demonstrated by the use of biomimetic micro-lattice design strategies to assemble 2D films into targeted 3D configurations. In terms of performance enhancement, ML/DL algorithms improve sensor accuracy and sensitivity by analyzing real-time noise signals and establishing hidden relationships between hydrogen concentration and signal noise. They also enhance the limit of detection (LOD) for sensors, as shown in experiments with hydrogen concentration sensors. ML/DL algorithms are widely applied in fiber Bragg grating sensors to improve key parameters such as range, signal-to-noise ratio, and accuracy. For instance, a long short-term memory (LSTM) neural network model converts recorded raw spectra into one- or two-dimensional data, enabling accurate pressure prediction. In calibration and compensation, ML/DL algorithms reduce signal drift caused by environmental factors and automatically compensate for various disturbances. For example, an automatic calibration algorithm based on rough set neural networks (RSNNs) models the sensor's response characteristics and calibrates the sensor's nonlinear response to temperature changes. Additionally, ML/DL algorithms are used to compensate for temperature-induced errors, as demonstrated by the use of a single-layer feedforward neural network (SLFN) to achieve high calibration accuracy and speed. In recognition and classification, ML/DL algorithms enable the identification and classification of objects and application scenarios. They reduce decision-making time in recognition, increase accuracy, lower the cost of manual identification, and minimize environmental interference for more precise feature extraction. For instance, ML/DL algorithms are used in robotic perception for gesture recognition, full-body motion detection, and tactile sensing. They are also used in object identification, such as distinguishing materials based on surface texture, and in human behavior recognition, such as posture recognition and activity classification. In health monitoring, ML/DL algorithms analyze vital information such as bloodThis review explores the integration of machine learning (ML) and deep learning (DL) algorithms with sensing technologies to enhance sensor performance, accuracy, sensitivity, and adaptability. The core of this advancement lies in the fusion of AI with sensor technology, enabling efficient algorithms that drive improvements in device performance and novel applications in biomedical and engineering fields. The review discusses the impact of ML/DL on sensor design, calibration and compensation, object recognition, and behavior prediction, highlighting their potential to significantly upgrade sensor functionalities and widen their application range. It also addresses challenges in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements. ML/DL algorithms assist in sensor design by enabling inverse design methods, such as using artificial neural networks to design target sensor configurations based on desired performance outcomes. They also optimize sensor performance during the design process by addressing issues like small measurement ranges, low signal-to-noise ratios, and inadequate precision. For example, a refined method using a functional link artificial neural network (FLANN) estimates unknown coefficients in a power series expansion, capturing the sensor's nonlinear response. Additionally, ML/DL algorithms improve device adaptability across various environmental conditions, as demonstrated by the use of biomimetic micro-lattice design strategies to assemble 2D films into targeted 3D configurations. In terms of performance enhancement, ML/DL algorithms improve sensor accuracy and sensitivity by analyzing real-time noise signals and establishing hidden relationships between hydrogen concentration and signal noise. They also enhance the limit of detection (LOD) for sensors, as shown in experiments with hydrogen concentration sensors. ML/DL algorithms are widely applied in fiber Bragg grating sensors to improve key parameters such as range, signal-to-noise ratio, and accuracy. For instance, a long short-term memory (LSTM) neural network model converts recorded raw spectra into one- or two-dimensional data, enabling accurate pressure prediction. In calibration and compensation, ML/DL algorithms reduce signal drift caused by environmental factors and automatically compensate for various disturbances. For example, an automatic calibration algorithm based on rough set neural networks (RSNNs) models the sensor's response characteristics and calibrates the sensor's nonlinear response to temperature changes. Additionally, ML/DL algorithms are used to compensate for temperature-induced errors, as demonstrated by the use of a single-layer feedforward neural network (SLFN) to achieve high calibration accuracy and speed. In recognition and classification, ML/DL algorithms enable the identification and classification of objects and application scenarios. They reduce decision-making time in recognition, increase accuracy, lower the cost of manual identification, and minimize environmental interference for more precise feature extraction. For instance, ML/DL algorithms are used in robotic perception for gesture recognition, full-body motion detection, and tactile sensing. They are also used in object identification, such as distinguishing materials based on surface texture, and in human behavior recognition, such as posture recognition and activity classification. In health monitoring, ML/DL algorithms analyze vital information such as blood