Cloud computing has become necessary to current undertakings, however the intricacy of cloud conditions presents critical difficulties in overseeing and keeping up with framework execution. Abnormalities in asset use, like equipment disappointments or misconfigurations, can prompt extreme execution debasement and administration disturbances. Customary irregularity location techniques frequently miss the mark in addressing these difficulties because of high misleading positive rates and restricted adaptability. This paper presents an original structure that coordinates semantic examination with deep learning, explicitly recurrent neural networks (RNNs), to improve the recognition of peculiarities in cloud conditions. By consolidating application semantics, our structure limits the extent of identification, decreasing computational above and further developing exactness. The RNN model is prepared on semantically labeled asset utilization information, permitting it to perceive typical examples and recognize deviations continuously. Exploratory outcomes, led by Apache Cassandra and MongoDB inside a cloud-based framework, show the viability of this methodology, accomplishing an identification precision of 98.3%. The framework likewise shows a 90% decrease in bogus up-sides and total disposal of misleading negatives in the wake of consolidating client criticism. These outcomes recommend that our structure can essentially further develop cloud unwavering quality, making it an important instrument for proactive cloud management.