A system for predicting and detecting of corrosion under insulation in an infrastructure asset using machine learning and data fusion comprising: at least one infrared camera positioned to capture thermal images of the asset; at least one smart mount mechanically supporting and electrically coupled to the at least one infrared camera and having a position that is adjustable with respect to the asset, the at least one smart mount including: a communication module; memory storage adapted to store thermal image data received from the at least one camera; a battery module operative to recharge the at least one infrared camera; an ambient sensor module adapted to obtain ambient condition data; and at least one additional sensor adapted to obtain CUI-related data from the asset; at least one computing device having a processor, the computing device being configured with instructions for executing a machine learning algorithm in the processor which receives input from either the communication module or memory storage of the at least one smart mount comprising: a) thermal images of the asset; b) the ambient condition data; c) CUI-related data from the at least one additional sensor providing data fusion; and d) result data obtained from conventional CUI tests, and which outputs a CUI prediction regarding the asset; and a supervised machine-learning platform adapted to: obtain the machine learning algorithm by training at least one of a convolution neural network and a deep recurrent neural network using a training data set comprising, for a plurality of assets, (i) thermal images of each asset, (ii) additional sensor probe data of each asset, (iii) ambient conditions at each asset, and (iv) known CUI result data; and provide the trained machine learning algorithm to the at least one computing device. at least one infrared camera positioned to capture thermal images of the asset; at least one smart mount mechanically supporting and electrically coupled to the at least one infrared camera and having a position that is adjustable with respect to the asset, the at least one smart mount including: a communication module; memory storage adapted to store thermal image data received from the at least one camera; a battery module operative to recharge the at least one infrared camera; an ambient sensor module adapted to obtain ambient condition data; and at least one additional sensor adapted to obtain CUI-related data from the asset; a communication module; memory storage adapted to store thermal image data received from the at least one camera; a battery module operative to recharge the at least one infrared camera; an ambient sensor module adapted to obtain ambient condition data; and at least one additional sensor adapted to obtain CUI-related data from the asset; at least one computing device having a processor, the computing device being configured with instructions for executing a machine learning algorithm in the processor which receives input from either the communication module or memory storage of the at least one smart mount comprising: a) thermal images of the asset; b) the ambient condition data; c) CUI-related data from the at least one additional sensor providing data fusion; and d) result data obtained from conventional CUI tests, and which outputs a CUI prediction regarding the asset; and a supervised machine-learning platform adapted to: obtain the machine learning algorithm by training at least one of a convolution neural network and a deep recurrent neural network using a training data set comprising, for a plurality of assets, (i) thermal images of each asset, (ii) additional sensor probe data of each asset, (iii) ambient conditions at each asset, and (iv) known CUI result data; and provide the trained machine learning algorithm to the at least one computing device. obtain the machine learning algorithm by training at least one of a convolution neural network and a deep recurrent neural network using a training data set comprising, for a plurality of assets, (i) thermal images of each asset, (ii) additional sensor probe data of each asset, (iii) ambient conditions at each asset, and (iv) known CUI result data; and provide the trained machine learning algorithm to the at least one computing device.