The field of signal detection and localization is moving towards innovative solutions that address the challenges of interference, noise, and complexity. Recent developments have focused on leveraging machine learning and deep learning techniques to improve the accuracy and robustness of detection and localization methods. Notably, transformer-based architectures have shown promising results in radar-based 3D pose estimation, while vision transformers have been applied to user equipment positioning. Additionally, clustering-based methods and reservoir computing have been explored for multi-transmitter localization and molecular communication detection. These advancements have the potential to significantly improve the performance of various applications, including Internet of Things (IoT) and molecular communication systems. Noteworthy papers include: Symbol Detection in Multi-channel Multi-tag Ambient Backscatter Communication Under IQ Imbalance, which proposes a novel symbol detection model that incorporates IQ imbalance parameters. RAPTR: Radar-based 3D Pose Estimation using Transformer, which achieves state-of-the-art results in indoor radar datasets. Vision Transformer Based User Equipment Positioning, which outperforms existing methods by 38%. Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications, which reduces localization error by 69%. Reservoir Computing-Based Detection for Molecular Communications, which achieves superior performance compared to complex ML methods with significantly fewer trainable parameters.