The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.