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Aiming ultra-reliable and mountable connectivity of tremendously high data rates in the Tb/s era with zero-perceived latency of 6G systems, next generation smart grids will need to captivate the advantages of breakthrough novel technology perceptions, incorporating THz wireless links, broadband and machine learning-based models designs, protocols and management practices. Motivated by the transformation potential of 6G systems, this paper deals with an island detection approach in 6G paradigm for an active distribution network. The proposed island detection method is tailored to adopt the breakthrough technologies of 6G, particularly, ultra-low latency, high volume data transmission and machine learned intelligent system. This methodology calculates the angular sum of the positive and the zero-sequence phase angle of the voltage at point of common coupling combined with random forest (RF) machine learning to identify islanding conditions. The technique has a robust resistance to noise due to the inherent nature of RF, has no consequences on power quality and exhibits a zero non-detection zone. It has the ability to differentiate between islanding and non-islanding events precisely even during active and reactive power discrepancy and transients similar to islanding. The method depicts a high testing accuracy of 98.46% with an island detection time of 52 ms. [ABSTRACT FROM AUTHOR]