This manuscript provides a comprehensive review of state-of-the-art techniques for multimodal image registration, focusing on scenarios where images from different modalities need to be precisely aligned in the same reference system. The review covers both classical and modern deep learning-based approaches, highlighting the specific challenges and requirements at each step of the registration pipeline. It emphasizes that medical images are excluded due to their unique characteristics, such as the use of active and passive sensors and the non-rigid nature of the body. The applications discussed include video surveillance, thermal insulation inspection, environmental monitoring, image filtering and fusion, crop inspection, and driving assistance systems. The process of image registration involves aligning images by identifying matching features and applying transformations, which becomes more challenging with multimodal images due to the different nature of the sources. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown promising results in handling noise and artifacts, making them effective for multimodal image registration. The manuscript also outlines a general image registration framework, detailing the steps involved in the process.This manuscript provides a comprehensive review of state-of-the-art techniques for multimodal image registration, focusing on scenarios where images from different modalities need to be precisely aligned in the same reference system. The review covers both classical and modern deep learning-based approaches, highlighting the specific challenges and requirements at each step of the registration pipeline. It emphasizes that medical images are excluded due to their unique characteristics, such as the use of active and passive sensors and the non-rigid nature of the body. The applications discussed include video surveillance, thermal insulation inspection, environmental monitoring, image filtering and fusion, crop inspection, and driving assistance systems. The process of image registration involves aligning images by identifying matching features and applying transformations, which becomes more challenging with multimodal images due to the different nature of the sources. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown promising results in handling noise and artifacts, making them effective for multimodal image registration. The manuscript also outlines a general image registration framework, detailing the steps involved in the process.