The IoT has become the foundation of present systems, where data collected by sensors are important information for such applications as smart cities, medical practice, and manufacturing. However, IoT data is always noisy or tainted with errors resulting from the environment it is placed in, the errors that occur when transmitting the data or the limitations of the sensors used. Elementary signal processing tools like Fast Fourier Transform, and filters for noise removal have been previously used but fail in handling complex noise model. This paper presents a new deep learning framework for noise reduction and for enhancing data in IoT systems with the help of Autoencoders to overcome these concerns. The presented methodology combines standard signal processing approaches with modern deep learning methods that facilitate the data enhancement, maintain informative features, and improve the quality of the signal representation. Experimental results prove that proposed Autoencoder-based method is much better compared to conventional techniques in term of SNR and MSE. In particular, the Autoencoder model improves the SNR by 5–10 [dB] and decreases the MSE by 40% as compared to the FFT-based method of denoising. These findings show how deep learning models can offer IoT applications a cleaner, less noisy data set due to its capacity to sort out problematic data.