Visible light is considered one of the foremost exciting areas of communication science and industry, gaining prominence over data broadcast and radiance instantaneously using low-cost light-emitting diodes (LEDs). Though, the high speed characteristics of this network are restricted by the truncated bandwidth of the LED. Therefore, amazing efficient, advanced modulation and demodulation schemes are considered for establishing high data rates in VLC. Carrierless amplitude-phase (CAP) modulation is such an attractive and effective modulation that it is gaining a good position due to its high efficiency and practical implementation. But multi-path scattering factors, noise factors, vigorous jamming and low sensitivity can have a significant impact on the performance of CAP-VLC systems. To overcome this problem, this paper examines the implementation of the CAP-VLC system based on the High Speed Feed Forward Neural Network, which operates on the principle of Extreme Learning Machines. The experiment was carried out by new simulated datasets used to train cap demodulators and parameters such as accuracy, retrieval, accurate bit error ratio (BER), noise ratio (SNR). Also the proposed learning based CAP-VLC systems has shown better performance such as 92.4% accuracy at various conditions, increase in BER by 40% and 50% of reduction of noise respectively.