## 1 Introduction
In the literature, several techniques have been proposed to improve spectral efficiency, OSNR, capacity, and the data rate of the FSO link [12; 13; 14; 15; 16; 17]. The authors of [12] modeled the cloud induced attenuation of Tokyo and Sendai regions of Japan by deriving its probability distribution function using curve-fitting techniques. The experimental evaluation of fog and haze climate conditions of a FSO link have been carried out under controlled conditions employing the [PERSON], [PERSON], and Alnaboulsi prediction models in [13]. The simulation results have been compared with experimental data, exhibiting the same trend of decrease in the received voltage levels with the increase in the intensity of fog or haze conditions. The authors of [2] analyzed the attenuation as a result of fog weather conditions in hilly regions of India. The PSK modulation technique results in better performance of the FSO link at a data rate and transmission wavelength of 40 Gbps and 1550 nm, respectively. A bidirectional WDM-FSO link has been investigated employing 16 quadrature amplitude modulation (QAM), orthogonal frequency division multiplexing (OFDM), and on-offkeying (OOK) modulation formats, considering the gamma-gamma channel model in [14]. A data rate of 320 Gbps has been achieved over a transmission range of 1000 m for possible data center applications. A 16 \(\times\) 10 Gbps FSO communication link has been analyzed for fog-induced attenuation in diverse regions by employing WDM with the OFDM technique [15]. A digital signal processing (DSP) module has been incorporated to mitigate channel-induced limitations, achieving a link range up to 10.75 km while maintaining a bit error rate of 10\({}^{-9}\). The authors of [16] estimated the prediction accuracy of received signal strength indicators (RSSI) on the receiver side of a FSO link by considering real time, non-linear atmospheric conditions in a maritime environment. Different models proposed in the literature predict the attenuation of the link based on the specific weather conditions of a region. Multi-impairments (fog, clouds, and haze) are time varying and depend on atmospheric and environmental variables. Regions with diverse meteorological conditions should be considered for analysis with an aim to evaluate the performance of the FSO link at high data rates under multi-impairment conditions Delhi, Washington, Cape Town, and London are meteorological regions and metropolitan cities with higher population densities and serve as hubs of information technology. The regions need better means of communication with higher achievable data rates under different impairments. Network planners must calculate the link length between the transmitter and receiver, link availability, outage probability, network dependability, and other factors based on the power margin/loss budget. It is important to investigate the performance of the link as a result of aggregate attenuation caused by different weather conditions. In the present work, data on fog, haze, and cloud climate conditions of four distinct geographical regions have been analyzed to derive a unified empirical model for the prediction of attenuation at optical wavelengths caused by multi-impairments in any region. These regions have greater population density and are hubs of information technology. The DP-QPSK modulation technique with Laguerre Gaussian (LG 00 and LG 01) modes has been employed to investigate the performance of the WDM-MDM-based FSO link under fog, haze, and cloud weather conditions. Accurate attenuation predictions may help determine the required transmitter power, antenna alignment receiver sensitivity, and other system parameters to achieve reliable communication. Further, performance estimation of the system has been achieved by employing random forest (RF), k-nearest neighbors (KNN), multi-layer perceptron (MLP), and gradient boosting (GB) machine learning (ML) techniques. The rest of the paper is organized as follows: Section 2 defines the different attenuation prediction models.